{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cpu:  16\n",
      "active []\n"
     ]
    }
   ],
   "source": [
    "# this notebook performs PCA \n",
    "import multiprocessing\n",
    "print \"cpu: \", multiprocessing.cpu_count()\n",
    "print 'active', multiprocessing.active_children()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import twx\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from numpy import linalg as LA\n",
    "%pylab inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "pulling data on date:  20150102 with align time interval of:  1\n",
      "Aligning by time interval: 1 seconds\n",
      "getting data from: AAPL\n",
      "getting data from: MSFT\n",
      "getting data from: GOOGL\n",
      "getting data from: FB\n",
      "getting data from: INTC\n",
      "getting data from: CSCO\n",
      "getting data from: NVDA\n",
      "getting data from: ADBE\n",
      "getting data from: TXN\n",
      "getting data from: AVGO\n",
      "getting data from: QCOM\n",
      "getting data from: MU\n",
      "getting data from: BIDU\n",
      "getting data from: AABA\n",
      "Ticker not found in the database!\n",
      "\n",
      "merge complete.\n",
      "\n",
      "[(2.256428322968869, array([0.15988328, 0.28468534, 0.35356584, 0.22083935, 0.31313216,\n",
      "       0.25623365, 0.11138879, 0.23825971, 0.28508223, 0.34774583,\n",
      "       0.27529313, 0.34384984, 0.30085234])), (1.297157444123453, array([-0.01070767, -0.35588322,  0.14596745,  0.01740269,  0.34480685,\n",
      "        0.46384022, -0.5948147 , -0.32044937, -0.1967961 ,  0.01669318,\n",
      "       -0.10078378,  0.10833346,  0.01379886])), (0.9743365272484568, array([-0.92035925,  0.14105019,  0.1041209 , -0.23745856, -0.03108281,\n",
      "        0.06351099, -0.04071599,  0.08667511,  0.05558163, -0.00936134,\n",
      "       -0.03881527,  0.18124736,  0.11879236])), (0.9474604584840937, array([-0.30889795, -0.00940604, -0.02475665,  0.87506771,  0.0880953 ,\n",
      "       -0.02625938,  0.10379573, -0.19197816,  0.08739618, -0.06378942,\n",
      "       -0.01541632, -0.15277884, -0.21626711])), (0.9117300301173529, array([ 0.08897348,  0.02347723,  0.11348053, -0.00674041,  0.14571168,\n",
      "        0.0025654 , -0.20710704,  0.42464896,  0.50031778, -0.26813383,\n",
      "       -0.57421161,  0.03060882, -0.28509863])), (0.8962443430586131, array([-0.02832551,  0.1102917 , -0.08094508, -0.20756301,  0.11707027,\n",
      "        0.14611217, -0.13178247, -0.08372686,  0.34959717, -0.22519993,\n",
      "        0.65784767, -0.12108788, -0.50755183])), (0.8677012936107539, array([-0.05811994, -0.23135219, -0.24321291,  0.1769833 ,  0.00299952,\n",
      "        0.13358778, -0.09507826,  0.74540728, -0.43003108, -0.04912148,\n",
      "        0.23670154,  0.09812213, -0.13077009])), (0.8579697846652904, array([ 0.00627155,  0.32570956,  0.00355158, -0.10429337,  0.14038509,\n",
      "        0.03771394,  0.15065643, -0.0919614 , -0.33055579,  0.44355503,\n",
      "       -0.23188438,  0.22736134, -0.64747812]))]\n",
      "\n",
      "\n",
      "\n",
      "pulling data on date:  20150102 with align time interval of:  5\n",
      "Aligning by time interval: 5 seconds\n",
      "getting data from: AAPL\n",
      "getting data from: MSFT\n",
      "getting data from: GOOGL\n",
      "getting data from: FB\n",
      "getting data from: INTC\n",
      "getting data from: CSCO\n",
      "getting data from: NVDA\n",
      "getting data from: ADBE\n",
      "getting data from: TXN\n",
      "getting data from: AVGO\n",
      "getting data from: QCOM\n",
      "getting data from: MU\n",
      "getting data from: BIDU\n",
      "getting data from: AABA\n",
      "Ticker not found in the database!\n",
      "\n",
      "merge complete.\n",
      "\n",
      "[(3.447990380725102, array([0.16025434, 0.310788  , 0.33214006, 0.20364003, 0.29702959,\n",
      "       0.27238908, 0.20243314, 0.26766514, 0.30037535, 0.30371349,\n",
      "       0.26378114, 0.33461147, 0.2947615 ])), (1.081177467865373, array([-0.21366524,  0.25438776, -0.11977953, -0.38358739, -0.2235728 ,\n",
      "       -0.21561271,  0.56768626,  0.32219612,  0.34224849, -0.27870389,\n",
      "       -0.03573472,  0.0424472 , -0.08779133])), (0.9780023331886211, array([ 0.80617974, -0.02026776, -0.04335018, -0.36392822, -0.06973477,\n",
      "       -0.14417585,  0.01996267, -0.07440608, -0.04072045, -0.09580819,\n",
      "        0.41166093, -0.05033618, -0.03033566])), (0.8687035295524623, array([ 0.16388789,  0.01982706, -0.06485991,  0.6187627 ,  0.22383199,\n",
      "       -0.00555512,  0.35733275, -0.14781834,  0.0976872 , -0.30538571,\n",
      "        0.09602054, -0.1441311 , -0.50319781])), (0.8559702611345259, array([-0.28959273,  0.01279896,  0.12381213, -0.33804226,  0.33961839,\n",
      "        0.14695842,  0.39262573, -0.61501182, -0.21110107,  0.01875149,\n",
      "        0.24384035, -0.06041554,  0.0949466 ])), (0.8082941891366785, array([-0.32503754, -0.28942776,  0.00650104, -0.04094363,  0.26071388,\n",
      "       -0.43017249, -0.28417267,  0.13240081,  0.14097502, -0.04482526,\n",
      "        0.54284836,  0.30582344, -0.21786183])), (0.7943686144669492, array([ 0.01146409, -0.13995985,  0.13149179, -0.34270181,  0.26793875,\n",
      "        0.63347477, -0.21891561,  0.23107878,  0.08687856, -0.37572755,\n",
      "       -0.09843325,  0.0317089 , -0.33425837]))]\n",
      "\n",
      "\n",
      "\n",
      "pulling data on date:  20150102 with align time interval of:  10\n",
      "Aligning by time interval: 10 seconds\n",
      "getting data from: AAPL\n",
      "getting data from: MSFT\n",
      "getting data from: GOOGL\n",
      "getting data from: FB\n",
      "getting data from: INTC\n",
      "getting data from: CSCO\n",
      "getting data from: NVDA\n",
      "getting data from: ADBE\n",
      "getting data from: TXN\n",
      "getting data from: AVGO\n",
      "getting data from: QCOM\n",
      "getting data from: MU\n",
      "getting data from: BIDU\n",
      "getting data from: AABA\n",
      "Ticker not found in the database!\n",
      "\n",
      "merge complete.\n",
      "\n",
      "[(4.249506080811331, array([-0.17292226, -0.32147773, -0.32055017, -0.1947695 , -0.30609812,\n",
      "       -0.28236347, -0.21701743, -0.2848358 , -0.32176105, -0.28627097,\n",
      "       -0.25970975, -0.3118097 , -0.27249497])), (1.0713715576103013, array([-0.09891813,  0.12089969, -0.20824654, -0.40030097, -0.16788145,\n",
      "       -0.08335065,  0.68647323,  0.1814413 ,  0.29748274, -0.35349173,\n",
      "        0.07392833,  0.04264407, -0.10934683])), (0.9798994413077161, array([ 0.78066179, -0.02688735,  0.04053909, -0.36707989, -0.02776054,\n",
      "       -0.04040435,  0.00644253, -0.18389923, -0.17348739,  0.05166414,\n",
      "        0.40293375, -0.12717046, -0.0767763 ])), (0.8425650129732791, array([-0.33556901, -0.15760387,  0.08962064, -0.72198153,  0.08907635,\n",
      "        0.3196605 , -0.25465042, -0.01178159, -0.07067019,  0.06131274,\n",
      "        0.08837333,  0.16673955,  0.33733775])), (0.7943137892854115, array([-0.29100481,  0.39833553, -0.03754627,  0.04153016,  0.19894354,\n",
      "       -0.23018645,  0.09774728, -0.58998295,  0.03948621,  0.00733482,\n",
      "        0.32098945, -0.32961737,  0.30002832])), (0.7727283984854136, array([-0.02474929, -0.10333339, -0.09774697,  0.12410239, -0.60128949,\n",
      "       -0.25014689,  0.1413429 ,  0.2063851 , -0.25031958,  0.2732541 ,\n",
      "        0.16947165,  0.05435835,  0.55502748]))]\n",
      "\n",
      "\n",
      "\n",
      "pulling data on date:  20150102 with align time interval of:  20\n",
      "Aligning by time interval: 20 seconds\n",
      "getting data from: AAPL\n",
      "getting data from: MSFT\n",
      "getting data from: GOOGL\n",
      "getting data from: FB\n",
      "getting data from: INTC\n",
      "getting data from: CSCO\n",
      "getting data from: NVDA\n",
      "getting data from: ADBE\n",
      "getting data from: TXN\n",
      "getting data from: AVGO\n",
      "getting data from: QCOM\n",
      "getting data from: MU\n",
      "getting data from: BIDU\n",
      "getting data from: AABA\n",
      "Ticker not found in the database!\n",
      "\n",
      "merge complete.\n",
      "\n",
      "[(5.035166203046435, array([0.19200916, 0.3063888 , 0.30428137, 0.22427399, 0.32380421,\n",
      "       0.28734528, 0.23823133, 0.29516914, 0.32186984, 0.27696854,\n",
      "       0.26224021, 0.27459531, 0.2656928 ])), (1.0071968375708698, array([-0.68446883, -0.16370651,  0.20246276, -0.16300142,  0.09812663,\n",
      "        0.17133733, -0.39811623,  0.06520483,  0.1245916 ,  0.11625773,\n",
      "       -0.24919896,  0.30145997,  0.23106556])), (0.8898832852864395, array([-0.39223556,  0.32452449, -0.25825441,  0.37948248,  0.09475497,\n",
      "        0.05404129,  0.39766063, -0.006614  ,  0.24293591, -0.36425339,\n",
      "       -0.37740491, -0.02014384, -0.15974201])), (0.8531800840515724, array([-1.58997097e-04, -5.08331081e-02, -1.10030541e-01, -6.67295208e-01,\n",
      "       -2.79948227e-01,  2.53129528e-01,  4.36966351e-01,  3.30082866e-01,\n",
      "        4.62026728e-02, -1.99726109e-01,  8.00102299e-03,  2.32532894e-01,\n",
      "       -3.90605735e-02])), (0.7337213514810175, array([ 0.0639643 , -0.11912051,  0.21325896,  0.04183476,  0.19197508,\n",
      "        0.24467725, -0.23159108, -0.07375307,  0.10933089, -0.29971498,\n",
      "        0.22393203,  0.28895661, -0.73706867]))]\n",
      "\n",
      "\n",
      "\n",
      "pulling data on date:  20150102 with align time interval of:  30\n",
      "Aligning by time interval: 30 seconds\n",
      "getting data from: AAPL\n",
      "getting data from: MSFT\n",
      "getting data from: GOOGL\n",
      "getting data from: FB\n",
      "getting data from: INTC\n",
      "getting data from: CSCO\n",
      "getting data from: NVDA\n",
      "getting data from: ADBE\n",
      "getting data from: TXN\n",
      "getting data from: AVGO\n",
      "getting data from: QCOM\n",
      "getting data from: MU\n",
      "getting data from: BIDU\n",
      "getting data from: AABA\n",
      "Ticker not found in the database!\n",
      "\n",
      "merge complete.\n",
      "\n",
      "[(5.435387511178339, array([0.20616074, 0.29121571, 0.28953237, 0.25233613, 0.31817724,\n",
      "       0.28766794, 0.24683757, 0.30186135, 0.31492926, 0.29388122,\n",
      "       0.27183746, 0.27004335, 0.23829469])), (0.995608268294771, array([-0.72054329,  0.1859515 ,  0.12216432,  0.18628277,  0.23562697,\n",
      "        0.21578603,  0.04891826, -0.08852757,  0.17089965, -0.24298815,\n",
      "       -0.43651692,  0.04161161,  0.06141444])), (0.8892180153394246, array([-0.19446221, -0.44304105,  0.2152063 , -0.41153367,  0.00868428,\n",
      "        0.19331209, -0.46005082,  0.138829  , -0.02171554,  0.23109728,\n",
      "       -0.01871574,  0.2828025 ,  0.38425914])), (0.7960567760218009, array([-0.01553727,  0.07924413, -0.09052238, -0.31869256, -0.30939147,\n",
      "       -0.16404292,  0.63105858,  0.16141727, -0.14364652, -0.06795065,\n",
      "       -0.24750539,  0.18304137,  0.4655979 ])), (0.775258590091101, array([-0.10696823,  0.08441533,  0.25480741,  0.40984069, -0.06548727,\n",
      "       -0.20317124, -0.08538238, -0.32267768, -0.41424164,  0.23027185,\n",
      "        0.13334342, -0.28949184,  0.51512156]))]\n",
      "\n",
      "\n",
      "\n",
      "pulling data on date:  20150102 with align time interval of:  60\n",
      "Aligning by time interval: 60 seconds\n",
      "getting data from: AAPL\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "getting data from: MSFT\n",
      "getting data from: GOOGL\n",
      "getting data from: FB\n",
      "getting data from: INTC\n",
      "getting data from: CSCO\n",
      "getting data from: NVDA\n",
      "getting data from: ADBE\n",
      "getting data from: TXN\n",
      "getting data from: AVGO\n",
      "getting data from: QCOM\n",
      "getting data from: MU\n",
      "getting data from: BIDU\n",
      "getting data from: AABA\n",
      "Ticker not found in the database!\n",
      "\n",
      "merge complete.\n",
      "\n",
      "[(6.116380915951728, array([0.22763177, 0.30663247, 0.30530572, 0.2691741 , 0.30743995,\n",
      "       0.28633982, 0.23905371, 0.29964278, 0.32429769, 0.26814013,\n",
      "       0.25585568, 0.256952  , 0.23769132])), (1.066693301644023, array([-0.5603054 , -0.1528235 ,  0.21572266,  0.18697873,  0.2641163 ,\n",
      "        0.37424537, -0.39379904,  0.06956893,  0.23475514, -0.27700446,\n",
      "       -0.25717934,  0.09252769, -0.07019236])), (0.8670869800143008, array([-0.12558498, -0.37802121,  0.10522656, -0.38215725, -0.28666486,\n",
      "        0.147343  ,  0.04229291,  0.34561985, -0.16189649,  0.2388796 ,\n",
      "       -0.20221251,  0.24583472,  0.52391275])), (0.8228015256239406, array([ 0.22220773, -0.21150751,  0.04290408, -0.4302466 ,  0.25436533,\n",
      "       -0.03787487, -0.48235196, -0.15429634,  0.13814386,  0.32899089,\n",
      "        0.24521232,  0.34124   , -0.30403264]))]\n",
      "\n",
      "\n",
      "\n",
      "pulling data on date:  20150102 with align time interval of:  120\n",
      "Aligning by time interval: 120 seconds\n",
      "getting data from: AAPL\n",
      "getting data from: MSFT\n",
      "getting data from: GOOGL\n",
      "getting data from: FB\n",
      "getting data from: INTC\n",
      "getting data from: CSCO\n",
      "getting data from: NVDA\n",
      "getting data from: ADBE\n",
      "getting data from: TXN\n",
      "getting data from: AVGO\n",
      "getting data from: QCOM\n",
      "getting data from: MU\n",
      "getting data from: BIDU\n",
      "getting data from: AABA\n",
      "Ticker not found in the database!\n",
      "\n",
      "merge complete.\n",
      "\n",
      "dataframe is empty\n",
      "\n",
      "\n",
      "\n",
      "pulling data on date:  20170103 with align time interval of:  1\n",
      "Aligning by time interval: 1 seconds\n",
      "getting data from: AAPL\n",
      "getting data from: MSFT\n",
      "getting data from: GOOGL\n",
      "getting data from: FB\n",
      "getting data from: INTC\n",
      "getting data from: CSCO\n",
      "getting data from: NVDA\n",
      "getting data from: ADBE\n",
      "getting data from: TXN\n",
      "getting data from: AVGO\n",
      "getting data from: QCOM\n",
      "getting data from: MU\n",
      "getting data from: BIDU\n",
      "getting data from: AABA\n",
      "Ticker not found in the database!\n",
      "\n",
      "merge complete.\n",
      "\n",
      "[(2.2101335795392174, array([-0.15699305, -0.29344519, -0.36592187, -0.22156606, -0.10330348,\n",
      "       -0.24832694, -0.18958321, -0.27582297, -0.35544857, -0.32510413,\n",
      "       -0.23331124, -0.34682205, -0.33932099])), (1.009774830690711, array([-0.06811648,  0.21939827,  0.00679042,  0.28711166,  0.7756354 ,\n",
      "       -0.15976881,  0.10407433,  0.11918071,  0.11868111, -0.26933292,\n",
      "        0.00079712, -0.30361994, -0.18374533])), (1.0051799834903226, array([-0.63493762, -0.1388885 , -0.09378066, -0.27974002,  0.26433442,\n",
      "        0.17281211,  0.32728866, -0.08583702, -0.24593277,  0.23371055,\n",
      "        0.3821536 ,  0.12040017,  0.02551887])), (0.9806650503862309, array([-0.42007847,  0.23921291,  0.09385179,  0.44655407, -0.44561761,\n",
      "        0.08308056,  0.46534048, -0.1119557 ,  0.05625486, -0.24914967,\n",
      "       -0.19910807, -0.05765477, -0.12381854])), (0.9613271679815145, array([-0.38095635, -0.26385627, -0.0427484 ,  0.24452644, -0.02195384,\n",
      "        0.47201168, -0.54180823,  0.41895321,  0.12746416, -0.08004618,\n",
      "       -0.05236146, -0.04058407, -0.06506364])), (0.9462151140785787, array([ 0.01669581, -0.12230762, -0.1314644 ,  0.48258651, -0.14547217,\n",
      "       -0.25479811, -0.20857066, -0.2444304 , -0.02786734, -0.07358249,\n",
      "        0.69326829, -0.11590332,  0.21215992])), (0.9223085050540624, array([-0.43243346,  0.50295096,  0.17430329, -0.19883978, -0.00396855,\n",
      "       -0.32845235, -0.49553617, -0.24431787,  0.1825548 ,  0.02026864,\n",
      "       -0.07340642,  0.17503274,  0.08495108])), (0.8926901667256962, array([-0.17504371, -0.19278569, -0.09034441, -0.00799603, -0.14957328,\n",
      "       -0.66496699,  0.13997198,  0.6305297 ,  0.10293222,  0.10318293,\n",
      "        0.00125107,  0.13473328, -0.05345852]))]\n",
      "\n",
      "\n",
      "\n",
      "pulling data on date:  20170103 with align time interval of:  5\n",
      "Aligning by time interval: 5 seconds\n",
      "getting data from: AAPL\n",
      "getting data from: MSFT\n",
      "getting data from: GOOGL\n",
      "getting data from: FB\n",
      "getting data from: INTC\n",
      "getting data from: CSCO\n",
      "getting data from: NVDA\n",
      "getting data from: ADBE\n",
      "getting data from: TXN\n",
      "getting data from: AVGO\n",
      "getting data from: QCOM\n",
      "getting data from: MU\n",
      "getting data from: BIDU\n",
      "getting data from: AABA\n",
      "Ticker not found in the database!\n",
      "\n",
      "merge complete.\n",
      "\n",
      "[(3.6017072795804705, array([0.17780004, 0.28897264, 0.34621485, 0.21735155, 0.34582031,\n",
      "       0.24958399, 0.2112472 , 0.25768107, 0.33977723, 0.29552172,\n",
      "       0.22165938, 0.32206715, 0.26310643])), (1.027863414277388, array([ 0.06389672,  0.35317641,  0.12959347,  0.25433309,  0.1316548 ,\n",
      "       -0.17465773,  0.61427413, -0.27890686,  0.05375421, -0.25299554,\n",
      "       -0.13808816, -0.35402996, -0.27466704])), (0.9624400548641181, array([ 0.09819116, -0.12441115, -0.120859  ,  0.32825547, -0.2360682 ,\n",
      "       -0.42539552,  0.17051835, -0.27115252, -0.23363359,  0.25106612,\n",
      "        0.51465415,  0.0210632 ,  0.36097184])), (0.9273792721909182, array([-0.93512159,  0.07175284,  0.04306319,  0.26989032, -0.08862174,\n",
      "        0.01608806,  0.03745592,  0.10658008,  0.10234856, -0.04876875,\n",
      "        0.09648773,  0.04076901,  0.03167667])), (0.855438123809678, array([-0.16935711,  0.2565463 ,  0.00839905, -0.3778529 ,  0.05462229,\n",
      "       -0.32163974, -0.00478751, -0.41055501,  0.19347067,  0.31838043,\n",
      "       -0.44440972,  0.14679307,  0.36027706])), (0.847913700775792, array([-0.15732388,  0.058836  , -0.10435032, -0.70709208,  0.07545415,\n",
      "        0.10682403,  0.36644576,  0.00443901, -0.0365411 ,  0.09107815,\n",
      "        0.5222682 , -0.06522145, -0.15120856])), (0.7867677335475061, array([-0.04360143, -0.26447523, -0.14022042,  0.12383397, -0.01485845,\n",
      "        0.71643949,  0.16966222, -0.56535002, -0.01299442,  0.08297722,\n",
      "       -0.07737077,  0.01132197,  0.13444117]))]\n",
      "\n",
      "\n",
      "\n",
      "pulling data on date:  20170103 with align time interval of:  10\n",
      "Aligning by time interval: 10 seconds\n",
      "getting data from: AAPL\n",
      "getting data from: MSFT\n",
      "getting data from: GOOGL\n",
      "getting data from: FB\n",
      "getting data from: INTC\n",
      "getting data from: CSCO\n",
      "getting data from: NVDA\n",
      "getting data from: ADBE\n",
      "getting data from: TXN\n",
      "getting data from: AVGO\n",
      "getting data from: QCOM\n",
      "getting data from: MU\n",
      "getting data from: BIDU\n",
      "getting data from: AABA\n",
      "Ticker not found in the database!\n",
      "\n",
      "merge complete.\n",
      "\n",
      "[(4.154985043925225, array([0.16264197, 0.28953898, 0.34532986, 0.23566405, 0.33999836,\n",
      "       0.26860855, 0.22841794, 0.25206966, 0.34031853, 0.30450857,\n",
      "       0.21126921, 0.31449987, 0.24366818])), (1.0813441442927507, array([ 0.43837377, -0.15267045, -0.0856009 ,  0.14281105, -0.19053858,\n",
      "       -0.24697012,  0.22841632, -0.36257825, -0.32289192,  0.16157357,\n",
      "        0.45449784,  0.04327053,  0.37021025])), (1.018900902370843, array([-0.16302199,  0.3544118 ,  0.05390368,  0.2668573 ,  0.11482076,\n",
      "       -0.14616326,  0.62510907, -0.33732762,  0.07588284, -0.16258075,\n",
      "       -0.07683187, -0.37895316, -0.22999904])), (0.906218637215639, array([-7.46099749e-01, -7.15063048e-02, -3.18634551e-04,  4.83025210e-01,\n",
      "       -1.89179593e-01, -7.54267513e-02, -1.33079690e-01, -8.57527879e-02,\n",
      "       -3.10314192e-02,  6.16388970e-02,  1.29572316e-01,  1.92385645e-01,\n",
      "        2.82494558e-01])), (0.8424363781307125, array([ 0.28809518, -0.12241005,  0.1209128 ,  0.3976704 , -0.20374939,\n",
      "        0.37667944,  0.01836881, -0.08545545, -0.06609507, -0.31688276,\n",
      "       -0.57502189,  0.0028957 ,  0.32058108])), (0.8163046282298051, array([-0.08512297, -0.10004811,  0.09254918, -0.32289335,  0.16598125,\n",
      "       -0.30561923,  0.03621183, -0.4320363 ,  0.24695557,  0.38252185,\n",
      "       -0.4987939 ,  0.15528437,  0.2842197 ]))]\n",
      "\n",
      "\n",
      "\n",
      "pulling data on date:  20170103 with align time interval of:  20\n",
      "Aligning by time interval: 20 seconds\n",
      "getting data from: AAPL\n",
      "getting data from: MSFT\n",
      "getting data from: GOOGL\n",
      "getting data from: FB\n",
      "getting data from: INTC\n",
      "getting data from: CSCO\n",
      "getting data from: NVDA\n",
      "getting data from: ADBE\n",
      "getting data from: TXN\n",
      "getting data from: AVGO\n",
      "getting data from: QCOM\n",
      "getting data from: MU\n",
      "getting data from: BIDU\n",
      "getting data from: AABA\n",
      "Ticker not found in the database!\n",
      "\n",
      "merge complete.\n",
      "\n",
      "[(4.820571081125465, array([-0.16392088, -0.2898542 , -0.34345192, -0.23926919, -0.34035325,\n",
      "       -0.2848015 , -0.22241655, -0.28814134, -0.32000707, -0.30380231,\n",
      "       -0.22112526, -0.30528636, -0.21831682])), (1.3375220086731507, array([ 0.42334291, -0.20105628,  0.02869108,  0.14985676, -0.26339743,\n",
      "       -0.08303387,  0.20399801, -0.3675233 , -0.37913819,  0.09721331,\n",
      "        0.39061596,  0.08890702,  0.4363875 ])), (0.9773365598822146, array([-0.09549785,  0.26610471,  0.08513026,  0.4986281 ,  0.07875832,\n",
      "       -0.20371389,  0.58233171, -0.25651551,  0.01582122, -0.3258733 ,\n",
      "       -0.18929129, -0.22303911, -0.13984741])), (0.8884881490101025, array([-0.62828895, -0.17484135,  0.0460039 ,  0.37753242, -0.03904407,\n",
      "       -0.24297708, -0.24305506, -0.15817713,  0.075049  ,  0.10534215,\n",
      "       -0.0790129 ,  0.38174852,  0.34157585])), (0.7636457668209501, array([-0.39044526,  0.09948672, -0.05858427, -0.26592387,  0.16601952,\n",
      "       -0.2476278 ,  0.22171023, -0.16341357, -0.0433252 ,  0.2922011 ,\n",
      "        0.63331874, -0.04005228, -0.32986433]))]\n",
      "\n",
      "\n",
      "\n",
      "pulling data on date:  20170103 with align time interval of:  30\n",
      "Aligning by time interval: 30 seconds\n",
      "getting data from: AAPL\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "getting data from: MSFT\n",
      "getting data from: GOOGL\n",
      "getting data from: FB\n",
      "getting data from: INTC\n",
      "getting data from: CSCO\n",
      "getting data from: NVDA\n",
      "getting data from: ADBE\n",
      "getting data from: TXN\n",
      "getting data from: AVGO\n",
      "getting data from: QCOM\n",
      "getting data from: MU\n",
      "getting data from: BIDU\n",
      "getting data from: AABA\n",
      "Ticker not found in the database!\n",
      "\n",
      "merge complete.\n",
      "\n",
      "[(5.003558040459268, array([0.19193009, 0.28605628, 0.34511514, 0.23143899, 0.32767082,\n",
      "       0.29406792, 0.22238277, 0.27174124, 0.32171696, 0.29549668,\n",
      "       0.22271687, 0.31594101, 0.22645765])), (1.414575397890859, array([-0.27548172,  0.13350283,  0.04802189, -0.15064284,  0.20901857,\n",
      "        0.13469953, -0.40201605,  0.45111104,  0.3230148 , -0.18156325,\n",
      "       -0.38250614,  0.06067138, -0.40870638])), (1.0597320240212595, array([ 0.23380591, -0.32870282, -0.13682765, -0.49125581, -0.12983833,\n",
      "        0.27134627, -0.4019297 ,  0.22463519, -0.15900181,  0.27463011,\n",
      "        0.30629621,  0.27375136,  0.0726601 ])), (0.8255048110863138, array([ 0.77101592,  0.12894863,  0.01261837, -0.20599351,  0.06859964,\n",
      "        0.13100768,  0.12034944,  0.14127534, -0.07052728, -0.23465257,\n",
      "       -0.33751301, -0.33937047,  0.02965212])), (0.7170151406700758, array([ 0.37192566, -0.25631596, -0.05547995,  0.41358309,  0.00893868,\n",
      "       -0.04028251, -0.03006282, -0.05037593,  0.10151392, -0.12464308,\n",
      "        0.3300285 ,  0.12632948, -0.68262832]))]\n",
      "\n",
      "\n",
      "\n",
      "pulling data on date:  20170103 with align time interval of:  60\n",
      "Aligning by time interval: 60 seconds\n",
      "getting data from: AAPL\n",
      "getting data from: MSFT\n",
      "getting data from: GOOGL\n",
      "getting data from: FB\n",
      "getting data from: INTC\n",
      "getting data from: CSCO\n",
      "getting data from: NVDA\n",
      "getting data from: ADBE\n",
      "getting data from: TXN\n",
      "getting data from: AVGO\n",
      "getting data from: QCOM\n",
      "getting data from: MU\n",
      "getting data from: BIDU\n",
      "getting data from: AABA\n",
      "Ticker not found in the database!\n",
      "\n",
      "merge complete.\n",
      "\n",
      "[(5.41838759842873, array([-0.20086839, -0.27542233, -0.32551751, -0.27769068, -0.30193781,\n",
      "       -0.2959661 , -0.23981785, -0.28442783, -0.28949117, -0.29586959,\n",
      "       -0.2337921 , -0.30708415, -0.25148188])), (1.4643994593113931, array([ 0.35385219, -0.21455135, -0.12111343, -0.09014435, -0.33006502,\n",
      "        0.26562888,  0.19288591, -0.28102556, -0.42420032,  0.09176314,\n",
      "        0.4995684 ,  0.06683856,  0.26053323])), (0.9979862360629346, array([ 0.04584733,  0.39147686,  0.07310433,  0.2802749 , -0.00331552,\n",
      "        0.00487936,  0.52819458, -0.17014351, -0.20309762, -0.36844481,\n",
      "       -0.26528655, -0.39779718,  0.21714681])), (0.7724570948950266, array([ 0.72142465,  0.06226788,  0.00896502,  0.09125651,  0.11052643,\n",
      "       -0.10133257,  0.03542878, -0.1414621 ,  0.05601196, -0.1222997 ,\n",
      "       -0.04558693,  0.08745722, -0.62903773]))]\n",
      "\n",
      "\n",
      "\n",
      "pulling data on date:  20170103 with align time interval of:  120\n",
      "Aligning by time interval: 120 seconds\n",
      "getting data from: AAPL\n",
      "getting data from: MSFT\n",
      "getting data from: GOOGL\n",
      "getting data from: FB\n",
      "getting data from: INTC\n",
      "getting data from: CSCO\n",
      "getting data from: NVDA\n",
      "getting data from: ADBE\n",
      "getting data from: TXN\n",
      "getting data from: AVGO\n",
      "getting data from: QCOM\n",
      "getting data from: MU\n",
      "getting data from: BIDU\n",
      "getting data from: AABA\n",
      "Ticker not found in the database!\n",
      "\n",
      "merge complete.\n",
      "\n",
      "dataframe is empty\n",
      "\n",
      "\n",
      "\n",
      "pulling data on date:  20170104 with align time interval of:  1\n",
      "Aligning by time interval: 1 seconds\n",
      "getting data from: AAPL\n",
      "getting data from: MSFT\n",
      "getting data from: GOOGL\n",
      "getting data from: FB\n",
      "getting data from: INTC\n",
      "getting data from: CSCO\n",
      "getting data from: NVDA\n",
      "getting data from: ADBE\n",
      "getting data from: TXN\n",
      "getting data from: AVGO\n",
      "getting data from: QCOM\n",
      "getting data from: MU\n",
      "getting data from: BIDU\n",
      "getting data from: AABA\n",
      "Ticker not found in the database!\n",
      "\n",
      "merge complete.\n",
      "\n",
      "[(2.1326596921813987, array([-0.09881265, -0.23779602, -0.3388278 , -0.25954614, -0.34911352,\n",
      "       -0.27107403, -0.2254443 , -0.24267779, -0.35952492, -0.23833617,\n",
      "       -0.24360535, -0.29206465, -0.34019699])), (1.0467684645320539, array([-0.7352396 ,  0.15025352,  0.30822544,  0.19570165,  0.19380031,\n",
      "       -0.1940124 ,  0.07456383, -0.01192525, -0.11337018, -0.44705529,\n",
      "       -0.08739265,  0.02640044,  0.03996885])), (1.0077019792599031, array([ 0.12656185,  0.02141076,  0.26374257, -0.44374143, -0.28285835,\n",
      "       -0.37047595, -0.0517278 ,  0.63193307, -0.02077873, -0.17849362,\n",
      "        0.04655719,  0.07906458,  0.23889354])), (0.9782336600041951, array([-0.31109239, -0.57080416, -0.02226315, -0.01311344,  0.06543996,\n",
      "        0.2988528 , -0.53553066,  0.17376923, -0.00609822,  0.043147  ,\n",
      "        0.38267339,  0.02033126,  0.13191349])), (0.9482444682275251, array([-0.29910539, -0.15266   ,  0.02887922, -0.17970984, -0.21536377,\n",
      "       -0.33092854,  0.35025744, -0.26656524, -0.12560512,  0.47705862,\n",
      "        0.36482562,  0.35694457, -0.02449407])), (0.9302372425399669, array([ 0.20134933,  0.41033208,  0.16348541,  0.34885738, -0.18797959,\n",
      "       -0.27535561, -0.49022503, -0.16731454, -0.10715106, -0.04497553,\n",
      "        0.48201752,  0.01477898, -0.13085048])), (0.9149820869547513, array([ 0.09518963, -0.10169646,  0.06703544,  0.03061027,  0.0170063 ,\n",
      "       -0.14930741, -0.39638767, -0.25555766,  0.06224037,  0.0241495 ,\n",
      "       -0.50964354,  0.64254071,  0.23043228])), (0.8988639576105667, array([ 0.33839279, -0.55110195,  0.12070865,  0.53533395, -0.0796097 ,\n",
      "       -0.16352888,  0.32443285,  0.0542783 , -0.22200962, -0.28182112,\n",
      "        0.05804944,  0.07243645,  0.04487258]))]\n",
      "\n",
      "\n",
      "\n",
      "pulling data on date:  20170104 with align time interval of:  5\n",
      "Aligning by time interval: 5 seconds\n",
      "getting data from: AAPL\n",
      "getting data from: MSFT\n",
      "getting data from: GOOGL\n",
      "getting data from: FB\n",
      "getting data from: INTC\n",
      "getting data from: CSCO\n",
      "getting data from: NVDA\n",
      "getting data from: ADBE\n",
      "getting data from: TXN\n",
      "getting data from: AVGO\n",
      "getting data from: QCOM\n",
      "getting data from: MU\n",
      "getting data from: BIDU\n",
      "getting data from: AABA\n",
      "Ticker not found in the database!\n",
      "\n",
      "merge complete.\n",
      "\n",
      "[(3.169781080555036, array([-0.10697878, -0.25380731, -0.36309611, -0.25219036, -0.32105444,\n",
      "       -0.2573622 , -0.2439752 , -0.25068445, -0.35802207, -0.22871369,\n",
      "       -0.24530491, -0.30177824, -0.32447869])), (1.1453689187622393, array([-0.49365698,  0.16830102, -0.11689111,  0.36143361,  0.30630373,\n",
      "       -0.13629978,  0.27599318, -0.476021  , -0.05366355, -0.24339076,\n",
      "       -0.12259953,  0.28640027, -0.09662986])), (1.0069298523645327, array([-0.47119802,  0.11740145,  0.28499384, -0.21545427,  0.04622955,\n",
      "        0.11470949,  0.11856598,  0.4481252 ,  0.13537137, -0.29536392,\n",
      "       -0.47160196, -0.27958573,  0.01535972])), (0.9349095278366265, array([-0.34289543, -0.02194829, -0.13846051, -0.33347647, -0.2841086 ,\n",
      "       -0.40440756,  0.46555589,  0.12631754,  0.07835882,  0.4701826 ,\n",
      "        0.15742499,  0.14849722,  0.0235734 ])), (0.9019073525046508, array([ 0.48856097,  0.57293173,  0.03723439,  0.08643329, -0.02461648,\n",
      "       -0.36324294,  0.31087577, -0.03235215,  0.06169987, -0.00589759,\n",
      "       -0.23421719, -0.26728584, -0.25262111])), (0.8759316502714153, array([ 0.30334367, -0.41394051,  0.1612095 , -0.24290125,  0.12780612,\n",
      "       -0.41457671,  0.2099216 , -0.06332139,  0.17540586, -0.48658833,\n",
      "       -0.04882968,  0.2040668 ,  0.3221776 ])), (0.8364570696410013, array([ 0.09098159,  0.19236596, -0.07742856, -0.36823617,  0.18055883,\n",
      "        0.0503781 , -0.2543362 , -0.24215035, -0.03971947,  0.3533742 ,\n",
      "       -0.58492612,  0.29805791,  0.31182276]))]\n",
      "\n",
      "\n",
      "\n",
      "pulling data on date:  20170104 with align time interval of:  10\n",
      "Aligning by time interval: 10 seconds\n",
      "getting data from: AAPL\n",
      "getting data from: MSFT\n",
      "getting data from: GOOGL\n",
      "getting data from: FB\n",
      "getting data from: INTC\n",
      "getting data from: CSCO\n",
      "getting data from: NVDA\n",
      "getting data from: ADBE\n",
      "getting data from: TXN\n",
      "getting data from: AVGO\n",
      "getting data from: QCOM\n",
      "getting data from: MU\n",
      "getting data from: BIDU\n",
      "getting data from: AABA\n",
      "Ticker not found in the database!\n",
      "\n",
      "merge complete.\n",
      "\n",
      "[(3.6355299298101347, array([-0.12254863, -0.27817537, -0.34955598, -0.28662042, -0.31863294,\n",
      "       -0.25490949, -0.24456029, -0.23473013, -0.34933763, -0.23740242,\n",
      "       -0.2603491 , -0.28074344, -0.31120839])), (1.1970297351939008, array([ 0.15649504, -0.12032194,  0.29885619, -0.37087341, -0.17953536,\n",
      "        0.20857853, -0.10899541,  0.64940643,  0.1359699 , -0.11134223,\n",
      "       -0.09918517, -0.42495691,  0.05926364])), (1.087435147562427, array([-0.5808113 ,  0.32150101,  0.16655708,  0.07380166,  0.16562526,\n",
      "       -0.10509953,  0.36050702,  0.05150917,  0.1451625 , -0.4071189 ,\n",
      "       -0.35218978, -0.16950697, -0.12418951])), (0.9316836486203484, array([ 0.66684961,  0.29185468,  0.08511701,  0.048512  , -0.02587734,\n",
      "       -0.32780014,  0.35431539, -0.10189166,  0.07428344, -0.0319618 ,\n",
      "       -0.03146238, -0.23156234, -0.39413154])), (0.8750739994968738, array([-0.00304598, -0.23220087, -0.01253681, -0.14983464, -0.36279727,\n",
      "       -0.30704624,  0.44744252,  0.05084177,  0.02484892, -0.40957579,\n",
      "        0.39685419,  0.22286761,  0.34531714])), (0.7972855186589957, array([-0.19028397,  0.33058825, -0.2215692 , -0.07311997, -0.2920283 ,\n",
      "        0.50961378,  0.2770064 , -0.05847573, -0.2018374 ,  0.12835518,\n",
      "        0.4511238 , -0.27372679, -0.19817383]))]\n",
      "\n",
      "\n",
      "\n",
      "pulling data on date:  20170104 with align time interval of:  20\n",
      "Aligning by time interval: 20 seconds\n",
      "getting data from: AAPL\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "getting data from: MSFT\n",
      "getting data from: GOOGL\n",
      "getting data from: FB\n",
      "getting data from: INTC\n",
      "getting data from: CSCO\n",
      "getting data from: NVDA\n",
      "getting data from: ADBE\n",
      "getting data from: TXN\n",
      "getting data from: AVGO\n",
      "getting data from: QCOM\n",
      "getting data from: MU\n",
      "getting data from: BIDU\n",
      "getting data from: AABA\n",
      "Ticker not found in the database!\n",
      "\n",
      "merge complete.\n",
      "\n",
      "[(4.297194266985981, array([0.10311074, 0.29071126, 0.32719654, 0.29717276, 0.30074997,\n",
      "       0.27911956, 0.26101861, 0.26029811, 0.34895519, 0.2385298 ,\n",
      "       0.27069715, 0.26822396, 0.28522726])), (1.223388934337593, array([ 0.51575062, -0.24809665,  0.10395464, -0.16407354, -0.29601246,\n",
      "        0.16283362, -0.41328688,  0.37726923,  0.0156509 ,  0.39052541,\n",
      "        0.12308124, -0.19694699,  0.02746299])), (1.0238119387613929, array([-0.32882099,  0.29384663,  0.28121583, -0.20871998,  0.02292389,\n",
      "        0.19749775, -0.0304999 ,  0.44805089,  0.17538739, -0.22033983,\n",
      "       -0.30447458, -0.493577  , -0.16137047])), (0.9041926088145746, array([ 0.63029084,  0.25474138,  0.02664628,  0.11742656,  0.23603273,\n",
      "       -0.26235301,  0.17312548, -0.13769983,  0.16238348, -0.08040569,\n",
      "       -0.08135463, -0.22150673, -0.51122379])), (0.8114346775166782, array([ 0.13052453, -0.20679486,  0.34416165, -0.00678167,  0.25178154,\n",
      "        0.11365168, -0.36600745, -0.22932865,  0.17421829, -0.07216666,\n",
      "       -0.64201758,  0.31755271,  0.10127157])), (0.7783353140925992, array([-0.05557696, -0.21885569,  0.30818307,  0.31724806, -0.45164229,\n",
      "       -0.57061782,  0.22168485,  0.31198132,  0.14296302, -0.06015517,\n",
      "       -0.17362247,  0.1520373 ,  0.00333288]))]\n",
      "\n",
      "\n",
      "\n",
      "pulling data on date:  20170104 with align time interval of:  30\n",
      "Aligning by time interval: 30 seconds\n",
      "getting data from: AAPL\n",
      "getting data from: MSFT\n",
      "getting data from: GOOGL\n",
      "getting data from: FB\n",
      "getting data from: INTC\n",
      "getting data from: CSCO\n",
      "getting data from: NVDA\n",
      "getting data from: ADBE\n",
      "getting data from: TXN\n",
      "getting data from: AVGO\n",
      "getting data from: QCOM\n",
      "getting data from: MU\n",
      "getting data from: BIDU\n",
      "getting data from: AABA\n",
      "Ticker not found in the database!\n",
      "\n",
      "merge complete.\n",
      "\n",
      "[(4.512027273315487, array([0.07757143, 0.31775659, 0.32970765, 0.29036517, 0.30840402,\n",
      "       0.28699649, 0.26619042, 0.2381717 , 0.3373633 , 0.23325765,\n",
      "       0.22793935, 0.27814244, 0.31207137])), (1.3882183064162357, array([-0.5833374 ,  0.22891081, -0.03029879,  0.19803848,  0.22506042,\n",
      "       -0.02115711,  0.36685916, -0.350383  ,  0.0088271 , -0.40645083,\n",
      "       -0.23905474,  0.13189164, -0.13749032])), (1.0682094430213283, array([-0.33228242,  0.00623109,  0.41335161, -0.17839295, -0.30609128,\n",
      "       -0.13451766,  0.06000136,  0.56147178,  0.09458178, -0.08165706,\n",
      "       -0.43255005, -0.15875916,  0.16825803])), (0.9497010039728611, array([-0.40638984, -0.22705672, -0.12618917, -0.21709183, -0.19394568,\n",
      "        0.29647717, -0.18246638, -0.04860338, -0.24746779,  0.00949658,\n",
      "        0.17594739,  0.50031872,  0.46525613])), (0.7471997924929541, array([-0.15746999,  0.30696657, -0.10162967, -0.43318935,  0.07265402,\n",
      "       -0.29904221,  0.25217532,  0.11516166, -0.09369507, -0.13137493,\n",
      "        0.60884103, -0.29920527,  0.15869577]))]\n",
      "\n",
      "\n",
      "\n",
      "pulling data on date:  20170104 with align time interval of:  60\n",
      "Aligning by time interval: 60 seconds\n",
      "getting data from: AAPL\n",
      "getting data from: MSFT\n",
      "getting data from: GOOGL\n",
      "getting data from: FB\n",
      "getting data from: INTC\n",
      "getting data from: CSCO\n",
      "getting data from: NVDA\n",
      "getting data from: ADBE\n",
      "getting data from: TXN\n",
      "getting data from: AVGO\n",
      "getting data from: QCOM\n",
      "getting data from: MU\n",
      "getting data from: BIDU\n",
      "getting data from: AABA\n",
      "Ticker not found in the database!\n",
      "\n",
      "merge complete.\n",
      "\n",
      "[(4.810366252388185, array([0.05538328, 0.29347463, 0.32664834, 0.2872861 , 0.29960184,\n",
      "       0.29329658, 0.27441429, 0.268705  , 0.33613716, 0.18719918,\n",
      "       0.23425907, 0.32685361, 0.29755814])), (1.4647232239423507, array([-0.66560725,  0.21355769,  0.11768404,  0.07804322,  0.10336351,\n",
      "        0.02453006,  0.31393173, -0.13732217,  0.00121008, -0.43040196,\n",
      "       -0.42110873,  0.01155032,  0.00320763])), (1.1063657826134101, array([ 0.05063601, -0.36023937,  0.36381654,  0.1895783 , -0.48659041,\n",
      "        0.00369049, -0.22876646,  0.39295728, -0.01922279, -0.26298144,\n",
      "       -0.26607888,  0.27213395,  0.20357852])), (0.9432365313585438, array([-0.29603696, -0.05577871, -0.12582527, -0.35575689, -0.07389132,\n",
      "        0.24477374, -0.13317342, -0.22244281, -0.37384904,  0.0613991 ,\n",
      "        0.25765739,  0.34053047,  0.5553238 ])), (0.8317740364515852, array([-0.15387162, -0.17651691,  0.09045863,  0.49494666, -0.12290679,\n",
      "        0.24638472, -0.08672481, -0.43141257, -0.09895669,  0.57551152,\n",
      "       -0.25734478,  0.04109247, -0.11679029]))]\n",
      "\n",
      "\n",
      "\n",
      "pulling data on date:  20170104 with align time interval of:  120\n",
      "Aligning by time interval: 120 seconds\n",
      "getting data from: AAPL\n",
      "getting data from: MSFT\n",
      "getting data from: GOOGL\n",
      "getting data from: FB\n",
      "getting data from: INTC\n",
      "getting data from: CSCO\n",
      "getting data from: NVDA\n",
      "getting data from: ADBE\n",
      "getting data from: TXN\n",
      "getting data from: AVGO\n",
      "getting data from: QCOM\n",
      "getting data from: MU\n",
      "getting data from: BIDU\n",
      "getting data from: AABA\n",
      "Ticker not found in the database!\n",
      "\n",
      "merge complete.\n",
      "\n",
      "dataframe is empty\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\"\"\"\n",
    "this cell runs pca for our universe of stocks\n",
    "run the function definition cell below first !\n",
    "\n",
    "output in the format of a tuple of (eigen_value, eigen_vector), where eigen_value is a floating point number\n",
    "and eigen_vector is a np.array of size = dimension of the universe of stocks\n",
    "example:\n",
    "\n",
    "[(4.810366252388185, \n",
    "array([0.05538328, 0.29347463, 0.32664834, 0.2872861 , 0.29960184,\n",
    "       0.29329658, 0.27441429, 0.268705  , 0.33613716, 0.18719918,\n",
    "       0.23425907, 0.32685361, 0.29755814]))),\n",
    "       \n",
    "(0.8317740364515852, \n",
    "array([-0.15387162, -0.17651691,  0.09045863,  0.49494666, -0.12290679,\n",
    "        0.24638472, -0.08672481, -0.43141257, -0.09895669,  0.57551152,\n",
    "       -0.25734478,  0.04109247, -0.11679029])))]\n",
    "       \n",
    "would be a list of two eigen pairs\n",
    "\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "# define parameters for wrappers\n",
    "\n",
    "top_n = 15 # top 15 market cap tech tickers to choose from\n",
    "filename = './companylist.csv'\n",
    "pools = get_tech_tickers(filename, top_n)\n",
    "pools.remove('GOOG')\n",
    "dates = ['20150102','20170103','20170104']\n",
    "# dates = ['20170407']\n",
    "start_time = '9am'\n",
    "end_time = '4pm'\n",
    "mtype = 'T'\n",
    "group = 'directs'\n",
    "align_by_sec_interval_list = [1,5,10,20,30,60,120]\n",
    "# align_by_sec_interval_list = [5,25,60]\n",
    "output_pairs = False\n",
    "thre = 0.65\n",
    "\n",
    "# to use\n",
    "if __name__ == '__main__':\n",
    "    res = {}\n",
    "    for date in dates:\n",
    "        res[date] = {}\n",
    "        for align_by_sec_interval in align_by_sec_interval_list:\n",
    "            print '\\n\\n'\n",
    "            print 'pulling data on date: ', date, 'with align time interval of: ', align_by_sec_interval\n",
    "            df = get_pool_data(pools, date, group, start_time, end_time, mtype, align_by_sec_interval)\n",
    "            eig_pair = pca(df, output_pairs = output_pairs, thre = thre)\n",
    "            res[date][align_by_sec_interval] = eig_pair\n",
    "            with open('pca_res.txt','a') as f:\n",
    "                f.write(str(date) + ' ' + str(align_by_sec_interval) + ' seconds ' + \\\n",
    "                        'eigen_pairs: \\n' + str(eig_pair) + '\\n' )\n",
    "    with open('pca_res.txt','a') as f:\n",
    "        f.write('\\n\\n\\nAll of the results are: \\n\\n')\n",
    "        f.write(str(res))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "pulling data on date:  20170501 with align time interval of:  60\n",
      "Aligning by time interval: 60 seconds\n",
      "getting data from: AAPL\n",
      "getting data from: QCOM\n",
      "\n",
      "merge complete.\n",
      "\n",
      "AAPL_log_ret  QCOM_log_ret    0.035818\n",
      "optimal result for current setting is: \n",
      "\n",
      "AAPL_log_ret  QCOM_log_ret    0.035818\n",
      "dtype: float64\n",
      "\n",
      "\n",
      "\n",
      "pulling data on date:  20170502 with align time interval of:  60\n",
      "Aligning by time interval: 60 seconds\n",
      "getting data from: AAPL\n",
      "getting data from: QCOM\n",
      "\n",
      "merge complete.\n",
      "\n",
      "AAPL_log_ret  QCOM_log_ret    0.151721\n",
      "optimal result for current setting is: \n",
      "\n",
      "AAPL_log_ret  QCOM_log_ret    0.151721\n",
      "dtype: float64\n",
      "\n",
      "\n",
      "\n",
      "pulling data on date:  20170503 with align time interval of:  60\n",
      "Aligning by time interval: 60 seconds\n",
      "getting data from: AAPL\n",
      "getting data from: QCOM\n",
      "\n",
      "merge complete.\n",
      "\n",
      "AAPL_log_ret  QCOM_log_ret    0.028126\n",
      "optimal result for current setting is: \n",
      "\n",
      "AAPL_log_ret  QCOM_log_ret    0.028126\n",
      "dtype: float64\n",
      "\n",
      "\n",
      "\n",
      "pulling data on date:  20170504 with align time interval of:  60\n",
      "Aligning by time interval: 60 seconds\n",
      "getting data from: AAPL\n",
      "getting data from: QCOM\n",
      "\n",
      "merge complete.\n",
      "\n",
      "AAPL_log_ret  QCOM_log_ret    0.273585\n",
      "optimal result for current setting is: \n",
      "\n",
      "AAPL_log_ret  QCOM_log_ret    0.273585\n",
      "dtype: float64\n",
      "\n",
      "\n",
      "\n",
      "pulling data on date:  20170505 with align time interval of:  60\n",
      "Aligning by time interval: 60 seconds\n",
      "getting data from: AAPL\n",
      "getting data from: QCOM\n",
      "\n",
      "merge complete.\n",
      "\n",
      "AAPL_log_ret  QCOM_log_ret    0.289691\n",
      "optimal result for current setting is: \n",
      "\n",
      "AAPL_log_ret  QCOM_log_ret    0.289691\n",
      "dtype: float64\n",
      "[0.2896914996267863, AAPL_log_ret  QCOM_log_ret    0.289691\n",
      "dtype: float64]\n",
      "{'20170503': {60: AAPL_log_ret  QCOM_log_ret    0.028126\n",
      "dtype: float64}, '20170502': {60: AAPL_log_ret  QCOM_log_ret    0.151721\n",
      "dtype: float64}, '20170501': {60: AAPL_log_ret  QCOM_log_ret    0.035818\n",
      "dtype: float64}, '20170505': {60: AAPL_log_ret  QCOM_log_ret    0.289691\n",
      "dtype: float64}, '20170504': {60: AAPL_log_ret  QCOM_log_ret    0.273585\n",
      "dtype: float64}}\n"
     ]
    }
   ],
   "source": [
    "# this cell is for getting the most correlated pairs of stocks !\n",
    "\n",
    "# run the function definition cell below first !\n",
    "# define parameters for wrappers\n",
    "\n",
    "top_n = 15 # top 100 market cap tech tickers to choose from\n",
    "filename = './companylist.csv'\n",
    "# pools = get_tech_tickers(filename, top_n)\n",
    "pools = ['AAPL', 'QCOM']\n",
    "# pools.remove('GOOG')\n",
    "# dates = ['20150102','20170103','20170104']\n",
    "# '20170407','20170410','20170411','20170412', '20170413', \n",
    "dates = ['20170501', '20170502','20170503','20170504','20170505']\n",
    "# '20170407'\n",
    "start_time = '9am'\n",
    "end_time = '4pm'\n",
    "mtype = 'T'\n",
    "group = 'directs'\n",
    "align_by_sec_interval_list = [60]\n",
    "# align_by_sec_interval_list = [60,65]\n",
    "output_pairs = True\n",
    "\n",
    "# to use\n",
    "if __name__ == '__main__':\n",
    "    res = {}\n",
    "    best_pair = ['0']*2 # (corr, corr_pair)\n",
    "    for date in dates:\n",
    "        res[date] = {}\n",
    "        for align_by_sec_interval in align_by_sec_interval_list:\n",
    "            print '\\n\\n'\n",
    "            print 'pulling data on date: ', date, 'with align time interval of: ', align_by_sec_interval\n",
    "            df = get_pool_data(pools, date, group, start_time, end_time, mtype, align_by_sec_interval)\n",
    "#             print df\n",
    "            pairs = pca(df, output_pairs = True)\n",
    "            print pairs.to_string()\n",
    "#             with open('pairs_res0605afternoon.txt','a') as f:\n",
    "#                 f.write(str(date)+str(align_by_sec_interval))\n",
    "#                 f.write(pairs.to_string())\n",
    "            print 'optimal result for current setting is: \\n\\n', pairs\n",
    "            res[date][align_by_sec_interval] = pairs\n",
    "            with open('pairs_res0603.txt','a') as f:\n",
    "                f.write(str(date) + ' ' + str(align_by_sec_interval) + ' seconds ' + str(pairs) + '\\n')\n",
    "            if float(pairs[0]) > float(best_pair[0]):\n",
    "                best_pair[0] = pairs[0]\n",
    "                best_pair[1] = pairs\n",
    "    print best_pair\n",
    "    print res\n",
    "    with open('pairs_res0604.txt','a') as f:\n",
    "        f.write('\\n\\n\\n best pair is ' + str(best_pair))\n",
    "        f.write('\\n\\n\\n final results are: ' + str(res))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('20170407', 0.701826), ('20170410', 0.421288), ('20170411', 0.386065), ('20170412', 0.302192), ('20170413', 0.177802), ('20170417', 0.211071), ('20170418', 0.266899), ('20170419', 0.229156), ('20170420', 0.089029), ('20170421', 0.41426), ('20170424', 0.329515), ('20170425', 0.289259), ('20170426', 0.491594), ('20170427', 0.193785), ('20170428', 0.283725), ('20170501', 0.035818), ('20170502', 0.151721), ('20170503', 0.028126), ('20170504', 0.273585), ('20170505', 0.289691)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa8dcb25cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 60 second interval : \n",
    "\n",
    "data = {\n",
    "'20170407': 0.701826, \n",
    "'20170410': 0.421288,\n",
    "'20170411': 0.386065,\n",
    "'20170412': 0.302192,\n",
    "'20170413': 0.177802,\n",
    "'20170417': 0.211071,\n",
    "'20170418': 0.266899,\n",
    "'20170419': 0.229156,\n",
    "'20170420': 0.089029,\n",
    "'20170421': 0.41426,\n",
    "'20170424': 0.329515,\n",
    "'20170425': 0.289259,\n",
    "'20170426': 0.491594,\n",
    "'20170427': 0.193785,\n",
    "'20170428': 0.283725,\n",
    "'20170501': 0.035818,\n",
    "'20170502': 0.151721,\n",
    "'20170503': 0.028126,\n",
    "'20170504': 0.273585,\n",
    "'20170505': 0.289691,\n",
    "}\n",
    "\n",
    "import sys\n",
    "\n",
    "def asint(s):\n",
    "    try: return int(s), ''\n",
    "    except ValueError: return sys.maxint, s\n",
    "    \n",
    "data = [(k, data[k]) for k in sorted(data, key=asint)]\n",
    "print data\n",
    "x = np.array(range(len(data)))\n",
    "myticks = [d[0] for d in data]\n",
    "y = np.array([d[1] for d in data])\n",
    "plt.xticks(x, myticks, rotation='45')\n",
    "plt.grid(True)\n",
    "plt.title('Correlation between QCOM vs. AAPL for 60 Seconds Interval over %d days' %len(data))\n",
    "plt.plot(x, y)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# QCOM_log_ret   AAPL_log_ret     0.701826 on  20170407 60 seconds interval\n",
    "date = '20170407'\n",
    "pool = ['aapl', 'qcom']\n",
    "start_time = '9am'\n",
    "end_time = '4am'\n",
    "mtype = 'T'\n",
    "group = 'directs'\n",
    "align_by_sec_interval = 60\n",
    "df = get_pool_data(pool, date, group, start_time, end_time, mtype, align_by_sec_interval)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa8dd8df550>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa8ddd1d310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import seaborn\n",
    "seaborn.mpl.rcParams['figure.figsize'] = (12.0, 8.0)\n",
    "seaborn.mpl.rcParams['savefig.dpi'] = 90\n",
    "# df_upper = df[:len(df)/2]\n",
    "# plt.title('Most Correlated High Frequency Pair QCOM vs. AAPL 1-day Window')\n",
    "df_upper.plot(title='Most Correlated High Frequency Pair QCOM vs. AAPL 1-day Window')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aligning by time interval: 60 seconds\n",
      "getting data from: aapl\n",
      "getting data from: qcom\n",
      "\n",
      "merge complete.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# QCOM_log_ret   AAPL_log_ret     0.701826 on  20170407 65 seconds interval\n",
    "date = '20170407'\n",
    "pool = ['aapl', 'qcom']\n",
    "start_time = '10:35am'\n",
    "end_time = '12pm'\n",
    "mtype = 'T'\n",
    "group = 'directs'\n",
    "align_by_sec_interval = 60\n",
    "df = get_pool_data(pool, date, group, start_time, end_time, mtype, align_by_sec_interval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa8dd8fd710>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa8dd944690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import seaborn\n",
    "seaborn.mpl.rcParams['figure.figsize'] = (12.0, 8.0)\n",
    "seaborn.mpl.rcParams['savefig.dpi'] = 90\n",
    "# df_upper = df[:len(df)/2]\n",
    "# plt.title('Most Correlated High Frequency Pair QCOM vs. AAPL 1-day Window')\n",
    "df.plot(title='Most Correlated High Frequency Pair QCOM vs. AAPL from 10:35am-12pm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# definition of all the functions used \n",
    "\n",
    "def get_ticker_data(date, group, ticker, start_time, end_time, mtype):\n",
    "    \"\"\"\n",
    "    returns data from tickquery\n",
    "    \"\"\"\n",
    "    try: \n",
    "        df = twx.tickquery(date, group, ticker, start_time, end_time, mtype)\n",
    "    except:\n",
    "        print \"Ticker not found in the database!\"\n",
    "        return None\n",
    "    return df\n",
    "    \n",
    "\n",
    "def align_time(df, align_by_sec_interval = 1):\n",
    "    \"\"\"\n",
    "    this function takes in output from the following function call:\n",
    "    \n",
    "    df = twx.tickquery('20150102', 'directs', 'aapl', '3pm', '4pm', 'T')[['price']]\n",
    "     \n",
    "    takes in a dataframe formatted as [time, price], where the time column is the index\n",
    "    returns a dataframe that aligns prices to each second interval\n",
    "    assume that the input column name is ['price']\n",
    "\n",
    "    \"\"\"\n",
    "    new_times = []\n",
    "    new_price = []\n",
    "    dates = df.index.tolist()\n",
    "    prices = df['price'].values.tolist()\n",
    "    ptr = 0\n",
    "    start_time = pd.Timestamp(df.index.values[0].astype('<M8[m]'))\n",
    "    end_time = pd.Timestamp(df.index.values[-1].astype('<M8[m]'))\n",
    "    l = pd.Timedelta(end_time - start_time).seconds/align_by_sec_interval\n",
    "#     if len(df) < l:\n",
    "#         print \"Warning ! the length of this ticker is less than length of the time interval.\\n\"\n",
    "    for idx in range(l):   \n",
    "        curr_time = start_time + pd.Timedelta(seconds=align_by_sec_interval) * idx\n",
    "        new_times.append(curr_time)\n",
    "        while ptr < len(prices) and dates[ptr] < curr_time:\n",
    "            # pointer that points to the last element before it reaches the end of that second\n",
    "            ptr += 1\n",
    "        # when it exits the while loop - dates[ptr] is now greater than or equal to curr second\n",
    "        # step back one step to make sure it is before end of current second interval \n",
    "        if ptr >= 1:\n",
    "            new_price.append(prices[ptr - 1])\n",
    "        else:\n",
    "            # ptr == 0\n",
    "            new_price.append(prices[0])\n",
    "    columns = df.columns.tolist()\n",
    "    assert len(columns) == 1\n",
    "    new_df = pd.DataFrame(index = new_times, columns = columns)\n",
    "    new_df[columns[0]] = new_price\n",
    "    return new_df\n",
    "\n",
    "\n",
    "def get_pool_data(pools, date, group, start_time, end_time, mtype, align_by_sec_interval = 1):\n",
    "    \"\"\"\n",
    "    this funtion takes in a list of tickers that we want to pull data from\n",
    "    and aligns the data using align_time function defined above so that price of each second interval\n",
    "    is extracted and aligned across different ticker\n",
    "    \n",
    "    params example:\n",
    "    pools = ['aapl', 'googl', 'msft']\n",
    "    date = '20150102'\n",
    "    start_time = '9am'\n",
    "    end_time = '4pm'\n",
    "    mtype = 'T'\n",
    "    group = 'directs'\n",
    "    \n",
    "    returns a dataframe that is the merged df of all ticker prices after proper time alignment and normalization\n",
    "    \n",
    "    returned df from this function can be fed directly into pca function to get most correlated pairs/eigenvalues\n",
    "    \"\"\"\n",
    "    d = {}\n",
    "    print \"Aligning by time interval: %d seconds\" % align_by_sec_interval\n",
    "    for idx, ticker in enumerate(pools):\n",
    "        print 'getting data from:', ticker\n",
    "        tmp = get_ticker_data(date, group, ticker, start_time, end_time, mtype)\n",
    "        if tmp is None: continue\n",
    "        tmp = tmp[['price']]\n",
    "        tmp = align_time(tmp, align_by_sec_interval)\n",
    "        # convert to log return\n",
    "        tmp['price'] = np.log(tmp.price).diff()\n",
    "        # careful that now the first entry is NAN value\n",
    "        tmp = tmp.dropna()\n",
    "        tmp = tmp.rename(columns={'price': ticker + '_log_ret'})\n",
    "        d[ticker] = tmp\n",
    "        \n",
    "        if idx == 0: \n",
    "            df = d[ticker]\n",
    "        else:\n",
    "            # merge current dataframe with existing large dataframe to df\n",
    "            df = pd.merge(d[ticker], df, left_index=True, right_index=True)\n",
    "    print \"\\nmerge complete.\\n\"\n",
    "    df=(df - df.mean())/df.std()\n",
    "    return df\n",
    "\n",
    "    \n",
    "def pca(df, output_pairs = True, thre = 0.65):\n",
    "    \"\"\"\n",
    "    thre - amount of contribution of eigenvalues to be output\n",
    "    \n",
    "    input data dimension N x D where N is number of tickers and D is sample size per ticker\n",
    "    at this point the index of datetime doesnt matter anymore since prices are already aligned \n",
    "    can simply take in price lists and compute correlation matrix and etc.\n",
    "    \n",
    "    if pairs is true, returns the pair of tickers that have the highest correlation\n",
    "    if pairs is false, returns the sorted eigenvectors and their eigenvalues\n",
    "    \n",
    "    if pairs is false - the funtion outputs the significant eigenvalues and eigenvectors\n",
    "    it outputs eigenvectors up to those that contribute to thre of significance\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    m = df.as_matrix()\n",
    "    D, N = m.shape\n",
    "    if D == 0:\n",
    "        print 'dataframe is empty'\n",
    "        if output_pairs:\n",
    "            return [0,0]\n",
    "        else:\n",
    "            return 0\n",
    "    \n",
    "    df_cor = df.corr().abs()\n",
    "    flat = df_cor.unstack()\n",
    "    cor_ordered = flat.order(kind=\"quicksort\")\n",
    "    \n",
    "    if output_pairs:\n",
    "        max_cor = cor_ordered[-(N+1):-N]    \n",
    "        return max_cor\n",
    "#         return cor_ordered\n",
    "    else:\n",
    "        cor = np.corrcoef(m.T)\n",
    "        assert cor.shape == (N,N)\n",
    "        eig_val, eig_vec = LA.eig(cor)  \n",
    "        # the column vec[:,i] is the eigenvector corresponding to the eigenvalue val[i].\n",
    "        # sort the eigenvector w.r.t its eigenvalues\n",
    "        ev_list = zip(eig_val, eig_vec.T)\n",
    "        ev_list.sort(key=lambda tup:tup[0], reverse = True)\n",
    "        eig_val, eig_vec = zip(*ev_list)\n",
    "        \n",
    "        tmp = 0.\n",
    "        idx = 0\n",
    "        while idx < len(eig_val) and tmp/sum(eig_val) < thre:\n",
    "            tmp += eig_val[idx]\n",
    "            idx += 1\n",
    "            \n",
    "        print 'length of extracted eigenvectors is:', idx\n",
    "       \n",
    "        return ev_list[:idx], df.columns.tolist()\n",
    "\n",
    "    \n",
    "def get_tech_tickers(filename = 'companylist.csv', top_n = 100):\n",
    "    \"\"\"\n",
    "    this function retrieves top 100 marketcap tech sector tickers from a csv file and returns a list of tickers\n",
    "    \"\"\"\n",
    "    l = pd.read_csv(filename)\n",
    "    tech = l[l['Sector'] == 'Technology']\n",
    "    tech = tech.sort(['MarketCap'], ascending=[0])[:top_n]\n",
    "    return tech['Symbol'].values.tolist()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c4b099ed0>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0c4b59c350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# optional - if want to visualize the aligned normalized log return from different tickers - \n",
    "# uncomment the following and run - adjust the size if needed\n",
    "\n",
    "# %matplotlib inline\n",
    "# import seaborn\n",
    "# seaborn.mpl.rcParams['figure.figsize'] = (12.0, 8.0)\n",
    "# seaborn.mpl.rcParams['savefig.dpi'] = 90\n",
    "# df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # this cell checks if a specific date is working \n",
    "\n",
    "\n",
    "# date = '20170506'\n",
    "# # '20170410','20170411','20170412'\n",
    "# df = twx.tickquery(date, 'directs', 'aapl', '3pm', '4pm', 'T')[['price']]\n",
    "# df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Name</th>\n",
       "      <th>LastSale</th>\n",
       "      <th>MarketCap</th>\n",
       "      <th>ADR TSO</th>\n",
       "      <th>IPOyear</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Summary Quote</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>Apple Inc.</td>\n",
       "      <td>188.36</td>\n",
       "      <td>9.258154e+11</td>\n",
       "      <td>n/a</td>\n",
       "      <td>1980</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Computer Manufacturing</td>\n",
       "      <td>https://www.nasdaq.com/symbol/aapl</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>Microsoft Corporation</td>\n",
       "      <td>98.66</td>\n",
       "      <td>7.580243e+11</td>\n",
       "      <td>n/a</td>\n",
       "      <td>1986</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Computer Software: Prepackaged Software</td>\n",
       "      <td>https://www.nasdaq.com/symbol/msft</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>GOOGL</td>\n",
       "      <td>Alphabet Inc.</td>\n",
       "      <td>1086.23</td>\n",
       "      <td>7.544397e+11</td>\n",
       "      <td>n/a</td>\n",
       "      <td>n/a</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Computer Software: Programming, Data Processing</td>\n",
       "      <td>https://www.nasdaq.com/symbol/googl</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>GOOG</td>\n",
       "      <td>Alphabet Inc.</td>\n",
       "      <td>1079.68</td>\n",
       "      <td>7.498904e+11</td>\n",
       "      <td>n/a</td>\n",
       "      <td>2004</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Computer Software: Programming, Data Processing</td>\n",
       "      <td>https://www.nasdaq.com/symbol/goog</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1027</th>\n",
       "      <td>FB</td>\n",
       "      <td>Facebook, Inc.</td>\n",
       "      <td>186.9</td>\n",
       "      <td>5.410059e+11</td>\n",
       "      <td>n/a</td>\n",
       "      <td>2012</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Computer Software: Programming, Data Processing</td>\n",
       "      <td>https://www.nasdaq.com/symbol/fb</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Symbol                   Name LastSale     MarketCap ADR TSO IPOyear  \\\n",
       "196    AAPL             Apple Inc.   188.36  9.258154e+11     n/a    1980   \n",
       "1984   MSFT  Microsoft Corporation    98.66  7.580243e+11     n/a    1986   \n",
       "123   GOOGL          Alphabet Inc.  1086.23  7.544397e+11     n/a     n/a   \n",
       "122    GOOG          Alphabet Inc.  1079.68  7.498904e+11     n/a    2004   \n",
       "1027     FB         Facebook, Inc.    186.9  5.410059e+11     n/a    2012   \n",
       "\n",
       "          Sector                                         Industry  \\\n",
       "196   Technology                           Computer Manufacturing   \n",
       "1984  Technology          Computer Software: Prepackaged Software   \n",
       "123   Technology  Computer Software: Programming, Data Processing   \n",
       "122   Technology  Computer Software: Programming, Data Processing   \n",
       "1027  Technology  Computer Software: Programming, Data Processing   \n",
       "\n",
       "                            Summary Quote  Unnamed: 9  \n",
       "196    https://www.nasdaq.com/symbol/aapl         NaN  \n",
       "1984   https://www.nasdaq.com/symbol/msft         NaN  \n",
       "123   https://www.nasdaq.com/symbol/googl         NaN  \n",
       "122    https://www.nasdaq.com/symbol/goog         NaN  \n",
       "1027     https://www.nasdaq.com/symbol/fb         NaN  "
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this cell shows the head of the companylist.csv file that is sorted by market cap\n",
    "\n",
    "l = pd.read_csv('./companylist.csv')\n",
    "tech = l[l['Sector'] == 'Technology']\n",
    "tech = tech.sort(['MarketCap'], ascending=[0])[:100]\n",
    "# print 'retrived a total of %d tech tickers ' %len(tech_tickers)\n",
    "tech.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "pulling data on date:  20150102 with align time interval of:  30\n",
      "Aligning by time interval: 30 seconds\n",
      "getting data from: AAPL\n",
      "getting data from: MSFT\n",
      "getting data from: GOOGL\n",
      "getting data from: FB\n",
      "getting data from: INTC\n",
      "getting data from: CSCO\n",
      "getting data from: NVDA\n",
      "getting data from: ADBE\n",
      "getting data from: TXN\n",
      "getting data from: AVGO\n",
      "getting data from: QCOM\n",
      "getting data from: MU\n",
      "getting data from: BIDU\n",
      "getting data from: AABA\n",
      "Ticker not found in the database!\n",
      "getting data from: ADP\n",
      "getting data from: ATVI\n",
      "getting data from: AMAT\n",
      "getting data from: INTU\n",
      "getting data from: CTSH\n",
      "getting data from: EA\n",
      "getting data from: NXPI\n",
      "getting data from: ADI\n",
      "getting data from: LRCX\n",
      "getting data from: ADSK\n",
      "\n",
      "merge complete.\n",
      "\n",
      "length of extracted eigenvectors is: 23\n"
     ]
    },
    {
     "data": {
      "image/png": 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fRmkTlr9qwXLToG5vQvcBR5nZxuGoyJ8Ib37hKNFNwJXhqDNmtqaZ9a3DeivcDYzA9wFmXr3jRuBiCydQmlmnUNCBPyLR2/wJRE3Nn1hU8Z/b18D6Get5GfjB/MmMzcysDNgb/4adT/V67fLgHuB3ZraJma2Ab1t4psoR3VFm1tb8SVLDw1iXY2b98C1H+znn3qryWAcz623+5MJmZnYUvqe01ku44bdDX3wLU1Wt8b94LQjb7Zw6rA98L+f+wGpmdiuAc24G8JKZXRZ+rszMNjSzipMZvwbWDsXVcsJRudOBC83s8Ix17Gpm14anVbQRLQz7ZrZrD48ws9XCfEZRzfauxf1ALzMbGPbxFmbW08zWCwXqPcA1YX9rama75BpgZs3N7K9m9hsza2L+RNvjgFfCtvgaaGXhxNLgHvz+tEb4Be18Kvfz0cCBZjYgrK+zmW2a8b1L8e9LC4AHw37UNvwct8a3h3wf/q2Le4BDzJ9U3Bz4MzDDOZfLpdN2D9u2hZn9Hn9uwMTwlH8Bg82fkNkG35Z1Z5W/nFWsaw8zO8LMOoblLfB/han4P+FG4G9mtnp4fDWrPLFzNFm2m/MnHz4BXBLe138DnEjN7ysV7+lv4wvNs8J27gv0rst2qYNs+8WN+P9/Ngzz62Bm+2cZV6b/4lu9rsa3nCwO97fB7wNzw9hPZtkDN1JCVDQL+Df2HYCXzV9x4UX8EZTTAJxzj+D71O41/yfct/An1BHeCPfAX8boK3yPX1lY74X4Prq38CfbvIL/s3Z1Mn9zPwn/ZjMLX/jcWsscHDDflr1O88jlnuTck/g3tQlhrC+GhyourzcK38/7UpjrOPwJkHUZM/iCYld8oTcv4/6r8EXWODP7LuRuH8b0Of7o9Gn4XuzX8T184HuwNzX/Z72HwlGe/cLzv8H3VR6RcRS1Lkei6vKchrx29R6Hc+4JfP/wY/j/JDvjz/rP9EQY02RgjHPuLrI7F3+U+umMfeLB8FhLfEE+G/+f5tHA3s65uhx1+9E592x4LarO43J8+8Rc/AlZT1T99myrDOtdjP85Wt/Mrg+PDQbaAdPCOu/FbxOAJ/F90LPNLLPFIHOsd+FbBY7HH+mbhf8FqKLg/zP+F7xv8b+oPpBlNffif16m47d7zh+I45ybi3/POAG/vb/Avz4VBf/xYWxv4f/S9KeaVlfN/b/i/+T9H/x8puHbDw4NY/ga/7P/bvh56or/uXwSvy9Nwx91PSs8fxq+BeMv+H7iSVS+F2T+ov1bfKvVvWE+x+OPVM7DH+UfVvPWCSt07lV8EXk7fhvtgP9FqrZ5V2iGP6I5B78tD8OfV/BhWP9L+PMnnsAf3V2I/6Uqm/lhXlPNbAF+37jJOXdDeLziROHnw/tZOeE9q5btNgzfbvE5frtf6Zx7uIY5ZW7ng/AHCObht1N1P/fVrifrA1n2C+fcaPx776Ph/4FX8O/r1a4vjHEMvpi/K+P+Sfi/IL2Jn3cHMq64I6Wl4kSehq3EXyj/SnwRfotz7tJqnncQ/kjfts651xocLNJA5v+U9jb+bPTljrhI8TB/6b+f8CeMFaJVJPXMbBZwYPjzvIhIqjX4SLP5azxei//Tzab4P/9snOV5rfFHD19qaKZIQ4Q/ETc3s/b4o43/VsEsIiIiNclHe8b2wAfOuU/Dnyvvxf/5uKq/4AuUUvqUOUmm4/B/wvwA/2fVfH8SnTSehv9pTHKh7S0iEmQ9gSRHa7LsJZS+IPRqVjD/6VVrOef+Y2bV9VCJFIRzbs/YY5DcOecW4S9FKAXinKvP5eVERBIpH0VztrNI/3d0wswMuIJlr36gS62IiIiISMnIR9H8BcteL3Etlr2OZxt8r3N5KKBXw5+Rum/VkwHNTH8KFBEREZGCcM7V+UBuPnqapwAbmtm6ZtYCf3mf/32qlXNugXOus3NufefcevgTAfep7uoZLtJHIw4ZMiRVuZpzOnLTOGdt63Rka87Jz9Wc05EbMztXDS6anb9o/HD89WzfBe51zr1nZueb2d7ZvgW1Z4iIiIhICclHewbOf2BE1yr3nVvNc3fPR2a+denSJVW5MbM153Rkpy03ZrbmnI7stOXGzNac05OdC30iYFBWVpaq3JjZmnM6stOWGzNbc05HdtpyY2ZrzunJzoWKZhERERGRWqhoFhERERGphdXn7MHGYmaumMYjIiIiIslkZrgCX3JORERERCTRVDQH5eXlqcqNma05pyM7bbkxszXndGSnLTdmtuacnuxcqGgWEREREamFeppFREREJHXU0ywiIiIikmcqmgP1ECU/N2a25pz83JjZmnM6stOWGzNbc05Pdi5UNIuIiIiI1EI9zSIiIiKSOuppFhERERHJMxXNgXqIkp8bM1tzTn5uzGzNOR3ZacuNma05pyc7FyqaRURERERqoZ5mEREREUkd9TSLiIiIiOSZiuZAPUTJz42ZrTknPzdmtuacjuy05cbM1pzTk50LFc0iIiIiIrVQT7OIiIiIpI56mkVERERE8kxFc6AeouTnxszWnJOfGzNbc05HdtpyY2ZrzunJzoWKZhERERGRWqinWURERERSRz3NIiIiIiJ5pqI5UA9R8nNjZmvOyc+Nma05pyM7bbkxszXn9GTnolnsAVQ1fDistx506VL5b/v2YHU+eC4iIiIikl9F19N85ZWOTz6BGTPgk0/8l5kvnjML6Yp/u3SBlVeOOWoRERERKTW59jQXXdFcdTzOwfz5voiuKKSr/tuy5fKFdObt1q0LOw8RERERKW6JOxHQDDp0gK23hgMOgFNPhWuugccfh3fegYULYfp0uPFGOOQQWHVVmDoV/vEPOPhg6NTJf22/PQwaBGecAddfD2PHwrRp8NNPPkc9RMnPjZmtOSc/N2a25pyO7LTlxszWnNOTnYui62nOlVllYbzddss/vnQpzJ697JHp11+Hhx/2tz/7zPdMr7girL46tGnjv1q3rrxd1+WWLQs+fREREREpgKJvz2hsS5fCzJkwdy58/73/Wriw8nYuy2a5F9prrgm77w7NSv7XFxEREZHSkbie5lLhHCxaVHuRXfWxadPg889hyBAYNgw23DD2TERERESSL3E9zYXS0H4aM1hhBd8mst56sMUW0KsX9O8PBx0ERx0FI0bA2WfDX/8K114Lt98Ol15azvjxsHgx7Lgj7LYb3HlnZa91Y1LfVPJzY2anLTdmtuacjuy05cbM1pzTk50LFc1FYJNN4P/+zx9xPuEEXzSvtZa/ZvXrr8cenYiIiIioPaNIffopjB4Nt94Kq6wCxxwDgwdDu3axRyYiIiJS+tTTnDC//grjx8Mtt8C4cbDvvr6A3nlnfUqiiIiISH2pp7meirWHqGlT6NcP7rsPPvgAuneH3/8eunaFSy+Fr75qvOzGksa+Kc05+bkxszXndGSnLTdmtuacnuxcqGguIZ06wSmn+A91uf12X0R36wYDB/oPe/nll9gjFBEREUkmtWeUuO+/hzFjfPvGZ5/B0KFw9NGwwQaxRyYiIiJSvNSekTJt2vge50mT4Kmn4McfoUcP6N0b7r4bfv459ghFRERESl9eimYz629m08zsfTMbleXx48zsLTN73cyeM7ON85GbT0noIdpsM7jiCvjiCzjuOH/1jbXWgpNOgjffbNzsXKSxb0pzTn5uzGzNOR3ZacuNma05pyc7Fw0ums2sCXAt0A/YFBicpSi+yzm3hXNuK+BvwBUNzZXqtWwJgwb5q2288gp06AD77APbbQc33gjffRd7hCIiIiKlpcE9zWbWAzjXObdnWD4TcM65S6t5/mDgcOfcXlkeU09zI/n1V3j6abj5Zn8Ju/3281fl2Hpr+M1voIkadURERCRFCn6dZjM7EOjnnDs2LB8ObO+cG1HleScApwDNgd2dcx9lWZeK5gKYPdv3O0+cCK+95pe7d4dttvFF9Dbb+EvaNWsWe6QiIiIijSPXojkfZVG2sOUqX+fcdcB1ZnYo8CdgaLaVDR06lC5dugDQrl07unfvTllZGVDZ89IYy5n9NIXIq1h+4403GDlyZMHyKpZ95JWceGJ3ttyyjNdfh/vuK2f0aLjwwjJmzoR11imna1fYZ58yttkG5swpp1mzhudX3FfI+VYsx9resfavzMxCb+8rr7yyYD+/xTBf7V+Fmy/E279ibm/tX9q/krh/QeG2d8XtGTNmUC/OuQZ9AT2AJzOWzwRG1fB8A76t5jEXy4QJE1KVW1v2d985V17u3N//7txhhznXrZtzrVo5t912zh13nHP//Kdzr7zi3M8/5ze3sel1Vm4SszXndGSnLTdmtuacjuxQd9a55s1He0ZTYDrQG5gFTAYGO+fey3jOhs65D8PtfYA/Oee2z7Iu19DxSONZuNBfhePVV31bx6uvwkcfwcYbL9vasfnmsOKKsUcrIiIiUr2C9zSH0P7AVfircdzinLvEzM4HpjjnHjezK4E+wGJgPjA8s6jOWI+K5hLz44/w9tvLFtLTp8NGG/kiuqKQ3nJLWGml2KMVERER8aJ8uIlz7knnXFfn3EbOuUvCfec65x4Pt0c65zZzzm3tnOudrWCOLbPfJQ25+cpu1Qp22AFOOMFfmeP112H+fP8JhT17+o/8Pvlk/xHgm24KRxwBl13W8Nz60uus3CRma87pyE5bbsxszTk92bnQ9REk71q2hG239V8VFi+GqVPhxRfhj3+EESNghRXijVFEREQkF3lpz8gXtWekQ//+8NvfwpFHxh6JiIiIpFWU9gyRXAwfDtdcA/r9SEREREqFiuZAPUSFs+KK5cybB5MnFz5br7Nyk5itOacjO225MbM15/Rk50JFsxRc06Zw4on+aLOIiIhIKVBPs0Qxfz6svz5Mmwarrhp7NCIiIpI26mmWktC+PQwaBP/8Z+yRiIiIiNRORXOgHqLC5w4fDjfcAEuWFD670NL8OqclN2a25pyO7LTlxszWnNOTnQsVzRLN5pv7Tw58+OHYIxERERGpmXqaJaoHHoCrr4bnnos9EhEREUkT9TRLSRk4ED75BN58M/ZIRERERKqnojlQD1Gc3GbN4PjjC3f5uWKYc1qy05YbM1tzTkd22nJjZmvO6cnOhYpmie53v4MHH4R582KPRERERCQ79TRLURgyBDbbDE4/PfZIREREJA1y7WlW0SxFYcoUf93mDz/0nxgoIiIi0ph0ImA9qYcobu522/lPBnziicJnF4Je5+TnxszWnNORnbbcmNmac3qyc6GiWYrG8OFw7bWxRyEiIiKyPLVnSNFYtAjWXRfKy2HjjWOPRkRERJJM7RlSslq29FfS+Mc/Yo9EREREZFkqmgP1EBVH7vHHw113wYIFhc9uTHqdk58bM1tzTkd22nJjZmvO6cnOhYpmKSprrgl9+sAdd8QeiYiIiEgl9TRL0Xn+ed+mMXUqNNGvdSIiItII1NMsJW+nnXx/8/jxsUciIiIi4qloDtRDVDy5ZnDSSY1z+blinXMSs9OWGzNbc05HdtpyY2ZrzunJzoWKZilKv/0tTJoEH38ceyQiIiIi6mmWInb66f7fv/0t7jhEREQkeXLtaVbRLEXrk0/8x2t/9hm0ahV7NCIiIpIkOhGwntRDVHy5660HO+4Id99d+Ox80+uc/NyY2ZpzOrLTlhszW3NOT3YuVDRLUTvpJLjmGtAfIERERCQmtWdIUVu6FDbZBG66CXbeOfZoREREJCnUniGJ0qQJDB/ujzaLiIiIxKKiOVAPUfHmHnmk/6CTL78sfHa+6HVOfm7MbM05Hdlpy42ZrTmnJzsXKpql6LVtC4cdBjfcEHskIiIiklbqaZaSMG0alJXBp5/6j9gWERERaQj1NEsibbwxbLEF3H9/7JGIiIhIGqloDtRDVPy5FZefi5HdUHqdk58bM1tzTkd22nJjZmvO6cnOhYpmKRkDBsDs2TB5cuyRiIiISNqop1lKyuWXw1tvwR13xB6JiIiIlLJce5pVNEtJmTcPNtgApk+Hzp1jj0ZERERKlU4ErCf1EJVGbocOcNBB/hMCC52iv7b8AAAgAElEQVTdEHqdk58bM1tzTkd22nJjZmvO6cnORV6KZjPrb2bTzOx9MxuV5fE/mNm7ZvaGmT1tZmvnI1fSafhwf83mX36JPRIRERFJiwa3Z5hZE+B9oDcwE5gCHOqcm5bxnF2Bl51zP5vZ8UCZc+7QLOtSe4bUyS67wIgR/qiziIiISK5itGdsD3zgnPvUObcEuBfYL/MJzrn/Oud+DosvAWvmIVdS7KST4NprY49CRERE0iIfRfOawOcZy19Qc1E8DBibh9y8Ug9RaeUOHAgffuivpFHo7PrQ65z83JjZmnM6stOWGzNbc05Pdi7yUTRnO6ydtcfCzA4HtgH+lodcSbHmzeH443W0WURERAqjWR7W8QWwTsbyWvje5mWYWR/gLGCX0MaR1dChQ+nSpQsA7dq1o3v37pSVlQGVv4k0xnJZWVmjrr+m5QqFzq+4r9DzzdfyJpuUc8klcOmlZbRvX9zbO+b+FWu54r5iGU+Sf561f6Vne2fOPQ3z1f5V2PzMuRcyv+K+QsyvvLycGTNmUB/5OBGwKTAdfyLgLGAyMNg5917Gc7YC7gf6Oec+qmFdOhFQcnLEEdC9O5x6auyRiIiISCkp+ImAzrlfgeHAOOBd4F7n3Htmdr6Z7R2edhmwEnC/mb1uZo80NDffqv6WlfTcmNn5zB0+HK67Dn79tfDZudDrnPzcmNmaczqy05YbM1tzTk92LvLRnoFz7kmga5X7zs24vUc+ckSq2mEH6NgRxo6Fvfeu/fkiIiIi9aGP0ZaSd8cdcPfd8OSTsUciIiIipSLX9gwVzVLyFi2CddeF//4Xunat/fkiIiIiMT7cJBHUQ1S6uS1bwjHH+N7mQmfXlV7n5OfGzNac05GdttyY2ZpzerJzoaJZEuH44+HOO+H772OPRERERJJI7RmSGAcfDLvtBiecEHskIiIiUuzUniGpNXy4/4RA/d4lIiIi+aaiOVAPUenn7rILNGsGzzxT+Oza6HVOfm7MbM05Hdlpy42ZrTmnJzsXKpolMcwqjzaLiIiI5JN6miVRfvjBX37ulVegS5fYoxEREZFipZ5mSbWVVoIhQ+D662OPRERERJJERXOgHqLk5J54Itx6K/z0U+Gzq6PXOfm5MbM153Rkpy03ZrbmnJ7sXKholsRZf33o0QPuuSf2SERERCQp1NMsifTUU3DmmfDaa/4EQREREZFM6mkWAfbYA378EV58MfZIREREJAlUNAfqIUpWbpMmvrf5mmsKn52NXufk58bM1pzTkZ223JjZmnN6snOholkSa+hQGDcOZs6MPRIREREpdepplkQ78URYZRU4//zYIxEREZFikmtPs4pmSbT33oPdd4dPP4UWLWKPRkRERIqFTgSsJ/UQJTO3WzfYdFN44IHCZ2fS65z83JjZmnM6stOWGzNbc05Pdi5UNEvinXTS8icEioiIiORC7RmSeL/+Chts4I82b7tt7NGIiIhIMVB7hkgVTZvCCSfAtdfGHomIiIiUKhXNgXqIkp07bBg8+ijssUc5p54Kl1wCt94Kjz8OkyfDJ5/ADz80Xr5e5+TnxszWnNORnbbcmNmac3qyc9Es9gBECqFjRygvhzFj/O3Zs2H6dP/vnDn+36+/9h+K0rmz/+rUqfJ2dcu6IoeIiEg6qKdZJHDOH22uKKIzv6reN2eO/2rVqvbCunNn2HBDWGGF2DMUERGRCrpOs0iBOAfffpu9qM68b9YsfwT7qadg7bVjj1pERERAJwLWm3qIkp+b72wzaN8eunaFnXaCAw6A44+HP//Zn3Q4ZgxMmADTpsGuu5bTqxdMnZq3+DpL2+uclP2rFHJjZmvOyc+Nma05pyc7FyqaRQrgkEPgwgtht91g0qTYoxEREZFcqT1DpID+8x8YMgRuvx0GDIg9GhERkfRSe4ZIERswAP79bzjqKLjjjtijERERkbpS0Ryohyj5uTGzM3N79vS9zuecA5dfXtjsQkpbbsxszTkd2WnLjZmtOacnOxe6TrNIBJtsAhMnQr9+/vrQl17qr7AhIiIixUk9zSIRzZ0Le+8Nv/kN3HwzNG8ee0QiIiLpoJ5mkRLSsSOMH++v6TxwYON+lLeIiIjUn4rmQD1Eyc+NmV1T7korwaOP+gK6Tx+YN69w2Y0pbbkxszXndGSnLTdmtuacnuxcqGgWKQLNm8Po0f5DUnbaCT7/PPaIREREJJN6mkWKzOWXwzXXwJNPQrdusUcjIiKSTLn2NOvqGSJF5rTToHNn/+mBjzwCPXrEHpGIiIioPSNQD1Hyc2Nm55p75JFwyy2wzz4wdmxhs/MlbbkxszXndGSnLTdmtuacnuxcqGgWKVJ77eU/PXDoULjzztijERERSTf1NIsUualToX9/GDkSTjkl9mhERESSIcp1ms2sv5lNM7P3zWxUlsd3NrNXzWyJmR2Qj0yRtKj49MCbboIzzgD9XikiIlJ4DS6azawJcC3QD9gUGGxmG1d52qfAEOCuhuY1FvUQJT83ZnZDc9deG154AZ57Do46CpYsKVx2faUtN2a25pyO7LTlxszWnNOTnYt8HGneHvjAOfepc24JcC+wX+YTnHOfOefeAXSMTKSeOnaEZ56B2bNh//3hxx9jj0hERCQ9GtzTbGYHAv2cc8eG5cOB7Z1zI7I89zbgMefcQ9WsSz3NIrVYsgSOPho+/hgeeww6dIg9IhERkdIT4zrN2cLqXfkOHTqULl26ANCuXTu6d+9OWVkZUHn4XstaTvvy7bfD4MHlbL01vPBCGWutVVzj07KWtaxlLWu52JYrbs+YMYN6cc416AvoATyZsXwmMKqa594GHFDDulwsEyZMSFVuzGzNOX8uu8y5ddZxburUwmfXJm25MbM153Rkpy03ZrbmnI7sUHfWuebNx5HmKcCGZrYuMAs4FBhcw/PrfBhcRGp2+umVnx746KOwww6xRyQiIpJMeblOs5n1B67Cn1h4i3PuEjM7H5jinHvczLYFHgbaAT8DXznnNs+yHpeP8YikzeOP+6tq/Otf/prOIiIiUrNce5r14SYiCfHii/6qGn//Oxx2WOzRiIiIFLcoH26SBJlN4mnIjZmtOTeOHXeEZ5+Fs86CK64obHY2acuNma05pyM7bbkxszXn9GTnIh89zSJSJDbd1H8ISr9+8NVXcMklsUckIiKSDGrPEEmgb76BvfeGDTeEE06AbbaBli1jj0pERKR4qKdZRAD44Qc491zfsjF9Omy1Fey0E/Tq5Vs5OnaMPUIREZF41NNcT+ohSn5uzOwYuSutBJdfDn//ezlffQXnnQcrrghXXw3rrQfdusExx8Do0fDBB5Dv31fTtK1jZ2vO6chOW27MbM05Pdm5UE+zSAq0aQN9+vgvgF9+gbffhokT4amn4E9/gsWL/RHoiqPRW28NLVrEHbeIiEixUHuGiADw2We+iJ440Z9M+OGHvnDu1csX0j17QocOsUcpIiKSH+ppFpG8WLAAXnqpspB++WVYZ53KIrpXL1h/fTB9xqeIiJQg9TTXk3qIkp8bM7sU59y2LfTtC+efD+PHw/z5cMcdsNlm8MQTsMsusPrqcOCB/gNVJk+GJUsanttQpbitSzU3ZrbmnPzcmNmac3qyc6GeZhGpk2bN/KXrttkGRozwJw5++mnlkejbb4ePPoJtt/VHoTt1gp13hqZNY49cRESk4dSeISJ58+23vqXjhRf80ehZs/xHex90EOy6qy+8RUREioF6mkWkaHz4ITz4IDzwAMyYAQMH+gJ6992hefPYoxMRkTRTT3M9qYco+bkxs9M65w03hFGjYMoU/7Xxxv560autBkcd5Y9GL1qU/9xYtH8lPzdmdtpyY2ZrzunJzoWKZhEpiC5d4NRTYdIkePNN/wmFl17qC+gjjoBHH4Wffoo9ShERkezUniEiUc2aBQ8/7Fs4XnsN9tzTt3DsuSe0ahV7dCIiklTqaRaRkjV7NjzyiC+gX37ZX/LuoINgwAD/qYYiIiL5op7melIPUfJzY2ZrznXTuTMceyyMGwcff+yPNt9xB6y1lj+J8M474bvv8p+bL6W0rUs9W3NOfm7MbM05Pdm5UNEsIkWpY0c4+mh/suCnn/oPUbn/fv+phHvvDaNHw7x5sUcpIiJpofYMESkpCxb4QvqBB/wnFfbs6Vs4Bg6EVVaJPToRESkV6mkWkdRYuBDGjvUF9FNP+U8jHDIEDj8crM5vgyIikkbqaa4n9RAlPzdmtubcOFq3hoMPhjFj/FU4hg+HP/+5nHPP9R/zXWhJ3tbFlq05Jz83ZrbmnJ7sXOhDbUUkEVZc0bdoAPzpT/7f88/XEWcREckPtWeISOLMmeM/qnvgQLjgAhXOIiKyvFzbM3SkWUQSp1MnePZZ6N3bt2n85S8qnEVEpGHU0xyohyj5uTGzNefC53bqBM88A//+N5xzTmF6nGPPOU3ZmnPyc2Nma87pyc6FjjSLSGJlHnEGuPBCHXEWEZH6UU+ziCTeN9/4wnmvveCii1Q4i4iIeppFRJazyiq+VaNPH9+mcfHFKpxFRCQ36mkO1EOU/NyY2Zpz/NyKwnnsWDjrrMbpcS62OSc5W3NOfm7MbM05Pdm50JFmEUmNjh2XPeJ8ySU64iwiInWjnmYRSZ25c33h3LevCmcRkbTSx2iLiNSiY0cYPx6efhpGjYrzkdsiIlJaVDQH6iFKfm7MbM25+HIrCufx4+GMM/JTOBf7nJOUrTknPzdmtuacnuxcqGgWkdTq0MEXzc8+C6efriPOIiJSPfU0i0jqzZsHe+wBZWVw+eXqcRYRSQP1NIuI5KjiiHN5OZx2mo44i4jI8lQ0B+ohSn5uzGzNufhz27f3hfN//wunnlq/wrnU5lzK2Zpz8nNjZmvO6cnOhYpmEZGgfXt/RY3nn4dTTtERZxERqaSeZhGRKr791vc49+oFV1yhHmcRkSRST7OISAO1a+ePOE+cCH/4g444i4iIiub/UQ9R8nNjZmvOpZdbUTi/+CKMHFm3wrnU51xK2Zpz8nNjZmvO6cnORV6KZjPrb2bTzOx9MxuV5fEWZnavmX1gZpPMbJ185IqINKZ27WDcOHjppboXziIikkwN7mk2sybA+0BvYCYwBTjUOTct4zm/BzZ3zp1gZocA+zvnDs2yLvU0i0jR+e476NsXdtgBrrpKPc4iIkkQo6d5e+AD59ynzrklwL3AflWesx9we7j9AL7AFhEpCSuv7I84v/wyjBihI84iImmUj6J5TeDzjOUvwn1Zn+Oc+xX41sw65CE7b9RDlPzcmNmac+nnVhTOU6bASSdlL5yTNudiztack58bM1tzTk92LprlYR3ZDmtX/e+k6nMsy3MAGDp0KF26dAGgXbt2dO/enbKyMqByoyZp+Y033oiW/8Ybb0SZf4W0be9YyxW0f+Vn+amnyujfH/bfv5yTT4bddqt8XPtX8vevmMvav7R/JXX/KtT2rrg9Y8YM6iMfPc09gPOcc/3D8pmAc85dmvGcseE5L5tZU2CWc65zlnWpp1lEit6CBdCvH2y9NVx7rXqcRURKUYye5inAhma2rpm1AA4F/l3lOY8BQ8Ltg4Fn85ArIhJF27bw1FPw2mtw4omwdGnsEYmISGNrcNEcepSHA+OAd4F7nXPvmdn5ZrZ3eNotwCpm9gEwEjizobn5VvVPQUnPjZmtOacjO+m5FYXzG29UFs5Jn3MxZWvOyc+Nma05pyc7F/noacY59yTQtcp952bcXgQMykeWiEixaNsWnnwS9twTTjgBBuldTkQksRrc05xP6mkWkVL0/few334wfToMGAB77QV9+kDr1rFHJiIi1cm1p1lFs4hInnzwATzxBDz+OEyeDD17+gJ6r71ggw1ij05ERDLFOBEwEdRDlPzcmNmac/JzAb78spyRI2H8ePjiCzjuOHjzTejVC7p1g9NOgwkTYMmS/OZq/0pHdtpyY2ZrzunJzoWKZhGRRtC2LRxwANxyC8ycCf/6F7RpA6NGQefOcPDBcPvtMHt27JGKiEhdqD1DRKTAvvoKxo71rRzjx0PXrpVtHFttBU10OENEpNGpp1lEpIQsXgwvvOD7oJ94wp9UmHkyYZs2sUcoIpJM6mmuJ/UQJT83ZrbmnPzc+ma3aAG77w5//7u/+sZzz8Hmm8P118Maa0DfvnDVVfDhh/nNzRe9zspNYrbmnJ7sXKhoFhEpIhtuCCefDOPG+V7o3/8e3n4bdtnFt3Gccgo8+6w/Qi0iIoWj9gwRkRKwdKn/9MGKNo7p0337xl57wd57Q6dOsUcoIlJa1NMsIpICs2dXnkz43//6q3P07Rt7VCIipUM9zfWkHqLk58bM1pyTn1vo7M6dYcgQuO8+OPvscoYMgSuvhEIfd9DrrNwkZmvO6cnOhYpmEZESt+WWMGkS3HYbDBsGixbFHpGISPKoPUNEJCEWLvRHn2fNgocegtVWiz0iEZHipfYMEZGUat0a7r/f9zZvvz289lrsEYmIJIeK5kA9RMnPjZmtOSc/N2Z2Zm6TJnDeef66z/36wZgxhcsupLS/zmnIjZmtOacnOxfNYg9ARETy76CD/DWfBw6Ed96B88/Xx3OLiDSEeppFRBJs9mw48EDo2NFflk4fyy0i4qmnWURE/qdzZ3jmGf/hJzvuCJ98EntEIiKlSUVzoB6i5OfGzNack58bM7u23BYt4J//hOOOg549IZ/DLNY5JzE7bbkxszXn9GTnQkWziEgKmMHw4XDXXXDIIXD99bFHJCJSWtTTLCKSMh99BPvuC7vsAldfDc2bxx6RiEjhqadZRERqtMEG/hMEv/gC9tgDvvkm9ohERIqfiuZAPUTJz42ZrTknPzdmdn1y27aFRx7xJwduvz289VbhsvNBr3Pyc2Nma87pyc6FimYRkZRq2hQuvhguugj69PFFtIiIZKeeZhER4ZVXYP/9/RU2/vhHf+KgiEiS5drTrKJZREQAmDXLF87rrAO33QYrrRR7RCIijUcnAtaTeoiSnxszW3NOfm7M7Hzlrr66v4bziivCzjvDZ58VLjtXep2TnxszW3NOT3YuVDSLiMj/rLACjB4Nhx8OPXrAxImxRyQiUhzUniEiIlk9+SQceSRccgkcfXTs0YiI5Jd6mkVEJG+mT/cfhLLnnnD55dCsWewRiYjkh3qa60k9RMnPjZmtOSc/N2Z2Y+Z27QovvQTTpsGAATB/fuGya6LXOfm5MbM15/Rk50JFs4iI1Kh9e3j8cdh8c9hhB3jvvdgjEhEpPLVniIhInY0eDWec4f8dMCD2aERE6k89zSIi0qgmTYKDDoKTT4aRI6FFi9gjEhHJnXqa60k9RMnPjZmtOSc/N2Z2oXN79oSXX4axY6Ft23J69YLTT4eHHvIfkFIIep2TnxszW3NOT3YudB60iIjkbK21YMIE+M9//LWdJ02CW26B3/0O2rSBHXf0xXXPnrDlltC8eewRi4g0jNozREQkb5yD99+HF1/0hfSkSfDJJ7DNNr6AriimO3WKPVIRSTv1NIuISFH57jvfzlFRRL/0EqyyyrJHozfbTNeAFpHCUk9zPamHKPm5MbM15+Tnxswu9jmvvDL07Qvnnus/ZXDePHj0UdhlF5gyBQYPhg4doHdvOOcceOIJmDu34bmNJW2vs7Z1OrLTOOdc6fd6EREpqCZNYNNN/dcxx/j75s3zR6AnTYIrroDJk2GNNSqPRO+4I2yyif9eEZEYGtSeYWbtgTHAusAMYJBz7rsszxsL9ACed87tW8P61J4hIiL8+iu8805lS8eLL8KcObD99r6A3nRTfwS7bVt/4mHbtv6rdWto2jT26EWkFBS0p9nMLgXmOucuM7NRQHvn3JlZnrcb0Ao4TkWziIjUx5w5/mj0iy/6kw2//x4WLFj264cfoFWryiK6alFd3Ve257RsGXvGItKYCt3TvB9we7h9OzAw25OccxOAhQ3MalTqIUp+bsxszTn5uTGz0zLnTp1gn33gr3+Fk04qZ9w4X0RPnQpffOGL5iVLYOZM397x0ENw9dUwahQcdhjsuitssIG/RN4338Abb/iPB7/hBvjTn+Coo2CPPXwbSJs2vmju1Ml/T/fuvgd7333h4IPLuflmX7zPn1+w6Wv/SkFuzOw0zjlXDe1p7uyc+xrAOfeVmekiQiIiEk2TJr7gbdMG1lyzYetatGj5I9nz5vmTGV94AW66yRfsrVtDt26+2N5kk8rbnTuD1fkYlogUu1rbM8zsaWDVzLsAB5wDjHbOdch47lznXMdq1rMrcKraM0REJCmc80e533vPF9BTp/rb777rC+ZsxfRaa6mYFikGubZn1Hqk2Tm3Rw1hX5vZqs65r81sNWB2XYOrM3ToULp06QJAu3bt6N69O2VlZUDl4Xsta1nLWtaylotlee214aOPyuneHUaO9I9PmFDO/PnQoUMZU6fC00+Xc8cdMGtWGT/8AGusUU6XLrD77mV06wYLFpSz2mrQu3f8+WhZy0ldrrg9Y8YM6sU5V+8v4FJgVLg9CrikhueWAY/Vsj4Xy4QJE1KVGzNbc05HdtpyY2ZrzqWVPW+ecxMnOnfTTc6dcopz/fs7t846zq24onNbbunc4MHOXXCBcw884NzUqc4tXpyf3Poq5W1darkxs9M451B31rnubWhP86XAfWZ2NPAZcDCAmW2Dv1LGsWH5OaAr0NrMPgOGOeeebmC2iIhIyWnf3l82b8cdl71/4UKYNq2yxeOOO/ztzz+H9deH1VaDV17xH0m+9db+knsiUjj6GG0REZEi9vPP/hJ7b77pi+ZXXvG311wTtt3WF9HbbgtbbeVPgBSRuinodZrzTUWziIhI7X75xR+VriiiX30V3noL1l23sojedlt/qbyVVoo9WpHiVOjrNCdGZpN4GnJjZmvO6chOW27MbM05HdmZuc2awWabwdChcO21/lMTv/0W7rkHyspg+nT4wx/8daarPu/HH+ufW2jFsK3Tkp3GOeeqoT3NIiIiUgSaN4ctt/Rfw4b5+xYv9h9H/uqr/oj0bbf5fumNNlq2tWOLLfyHvohI9dSeISIikiKLFsHbby/b2jF9Omy88bKtHZtvDi1axB6tSONRT7OIiIjk5KeffE90RRH9yivw4Yf+w1i23x5694bdd/dX/hBJCvU015N6iJKfGzNbc05+bsxszTkd2Y2Zu+KKsMMOcOKJcOutvoD+5hu45hqAcm65xZ9k2KMH/PnP8PzzsGRJow3nf5K4rYs1O41zzpV6mkVERGQ5rVpBz56+naOszP87cSI8/TSMHAkffQS77gp77AF9+/o+aX08uCSZ2jNEREQkZ3PmwDPPwLhxvpBu2rSygO7dGzp0iD1CkZqpp1lEREQKyjl/3eiKAvq55/yJhX37+q8ePXRSoRQf9TTXk3qIkp8bM1tzTn5uzGzNOR3ZxZxrBt26wcknw+OP+6PQl10GS5fCqaf660Xvs4/vkZ42zRfZ+cpuDNq/0pOdCxXNIiIiklctW/o+6IsvhilTfP/zEUf4j//u29efVDhsGIwZ4084FCkFas8QERGRgnEO3n/ft3KMG+dbOTbaqLKVo2dPX3SLNDb1NIuIiEjJWLwYXnqpsh/6vfdg5539SYUbbug/qbDiq2XLZZcrvpo105U7JHcqmuupvLycsrKy1OTGzNac05GdttyY2ZpzOrLTkjtvnr8qx/jx8NZb5ay0Uhk//+wveffzz9m/li6tW3Fd9au65y1cWM6ppxZuzpnS8joXQ3auRbOu0ywiIiJFo0MHOPhg/1Ve7nuja/PLL9UX1TUV25lfc+dW3n78cfjsM7j8cmjevLFnLKVCR5pFREREMsyfD4cfDgsXwn33waqrxh6RNAZdck5ERESkAdq3h8ce8594uN12MHly7BFJMVDRHOi6iMnPjZmtOSc/N2a25pyO7LTlxswuLy+nSRO44AK4+mrYay+45ZbCZceQxtc5VyqaRURERKoxcCA8/zz87W9w/PG+R1rSST3NIiIiIrVYsACGDIGvv4YHHoA11og9Imko9TSLiIiI5FnbtvDggzBggO9znjgx9oik0FQ0B+ohSn5uzGzNOfm5MbM153Rkpy03ZnZ1uU2awDnnwE03wQEHwHXX+U84LER2Y0vj65wrFc0iIiIiORgwwB9pvv56OPpof21nST71NIuIiIjUw8KFMGwYfPyxb91YZ53YI5JcqKdZREREpABat4Z774VBg2CHHWDChNgjksakojlQD1Hyc2Nma87Jz42ZrTmnIzttuTGzc8k1g9NPh3/9CwYPhiuuaFifcynMOUnZuVDRLCIiItJAffrASy/BHXf4j+D+8cfYI5J8U0+ziIiISJ78+CMcdxy8/TY8/DCst17sEUl11NMsIiIiEkmrVv5o89FHQ48eMG5c7BFJvqhoDtRDlPzcmNmac/JzY2ZrzunITltuzOyG5prBiBFw330wdChccknd+5xLdc6lmp0LFc0iIiIijWDXXWHyZN+mMWgQfP997BFJQ6inWURERKQR/fwzDB8OkybBI4/ARhvFHpGAeppFREREisoKK/iP3h4xAnr1gscfjz0iqQ8VzYF6iJKfGzNbc05+bsxszTkd2WnLjZndGLlm/qoajz4Kxx8PF1wAS5cWJrsu0vg650pFs4iIiEiB9OwJU6bAU0/B/vvDd9/FHpHUlXqaRURERAps8WL4wx9g/Hh/ouAmm8QeUfqop1lERESkyLVoAf/4B5x5pr/KxkMPxR6R1EZFc6AeouTnxszWnJOfGzNbc05HdtpyY2YXMveoo2DsWH/U+eyz4ZlnCpedKY2vc65UNIuIiIhEtO22vs954kT4y19g0aLYI5Js1NMsIiIiUgR+/hkGD4YffvDtGq1bxx5RsqmnWURERKQErbAC3H8/rL029OkDc+fGHpFkalDRbGbtzWycmU03s6fMbOUsz9nSzF40s7fN7A0zG9SQzMaShr6pYsnWnKofI4cAACAASURBVNORnbbcmNmaczqy05YbMzvmnF94oZybb4ZddvFfX35ZmNw0vs65auiR5jOB8c65rsCzwFlZnvMDcIRzbnNgT+BKM2vbwFwRERGRRDKDyy6DIUNgp53g/fdjj0iggT3NZjYN2NU597WZrQaUO+c2ruV73gAOdM59lOUx9TSLiIiIBLfcAuecA088AVtvHXs0yZJrT3OzBuZ1ds59DeCc+8rMOtUyuO2B5tkKZhERERFZ1rBh0L499O8P990HZWWxR5RetRbNZvY0sGrmXYADzsklyMxWB+4AjqjpeUOHDqVLly4AtGvXju7du1MW9pCKnpfGWM7spylEXsXyG2+8wciRIwuWl7l85ZVXFmz7Zi5X3Ffo+cbc3rH2r8xM7V+Nv6z9K/n7V8ztrf0rvftXhw7lnHkmDBpUxk03wcor5z8/DfVIxe0ZM2ZQL865en8B7wGrhturAe9V87w2wKvAAbWsz8UyYcKEVOXGzNac05GdttyY2ZpzOrLTlhszu1jnPGWKc6ut5txttxU2t7HFyg51Z53r3ob2NF8KzHPOXWpmo4D2zrkzqzynOfAk8Khz7upa1ucaMh4RERGRJJs2Dfr1gxEj4NRTY4+mtOXa09zQorkDcB+wNvAZcLBz7lsz2wY4zjl3rJkdBtwKvEtla8dQ59xbWdanollERESkBp9/Dn37wsCBcPHF/mobkruCfriJc26ec66Pc66rc24P59y34f5XnXPHhtt3OedaOue2ds5tFf5drmCOLbPfJQ25MbM153Rkpy03ZrbmnI7stOXGzC72Oa+9Njz/PDzzDBx3HPz6a2FyG0vM7Fw0qGgWERERkcJbZRVfNH/8MRxyCCxaFHtEydeg9ox8U3uGiIiISN0tWgSHHQbffgsPPwxt2sQeUekoaHuGiIiIiMTTsiWMGQPrrw+9e8M338QeUXKpaA7UN5X83JjZmnPyc2Nma87pyE5bbszsUptz06Zw443Qpw/svLM/UbAQuflSKj3NDf1EQBERERGJzMxfSaNjR9hpJ3jqKdh449ijShb1NIuIiIgkyOjRcNZZ8NhjsO22sUdTvNTTLCIiIpJiQ4fCDTfAgAHw7LOxR5McKpoD9U0lPzdmtuac/NyY2ZpzOrLTlhszOwlz3m8/uP9+OPRQeOihwuXWh3qaRURERCSaXXeFJ5+EvfeG+fNh2LDYIypt6mkWERERSbD334d+/eD3v4czzog9muKRa0+zimYRERGRhPvyS+jbF/baCy691F9tI+10ImA9qW8q+bkxszXn5OfGzNac05GdttyY2Umc85prwnPP+a/f/Q5++aUwuXVRKj3NKppFREREUqBjRxg/Hj77DAYNgp9/jj2i0qL2DBEREZEUWbQIjjwS5syBRx6Btm1jjygOtWeIiIiISLVatoS774auXWH33X3xLLVT0Ryobyr5uTGzNefk58bM1pzTkZ223JjZaZhz06Zw3XWw557+Y7fHjClMbjal0tOs6zSLiIiIpJAZ/OUvvtd5+HDYZBPYfPPYoype6mkWERERSbl774URI+C++6CsLPZoCkM9zSIiIiKSk0MPhTFj/FU1xoyJPZripKI5UN9U8nNjZmvOyc+Nma05pyM7bbkxs9M6591285ekO+00uOKKwmaXAhXNIiIiIgLAFlvAxIlw881w6qmwdGnsERUP9TSLiIiIyDLmzYOBA2GNNeD22/1l6pJGPc0iIiIi0iAdOsC4cf7jtvv3h2+/jT2i+FQ0B+qbSn5uzGzNOfm5MbM153Rkpy03Zrbm7K2wgj8pcPPNYZdd4MsvC5ddjFQ0i4iIiEhWTZvCVVfBEUfAjjvCu+/GHlE86mkWERERkVrddReccgo88ADsvHPs0TSceppFREREJO8OO8wXzgce6AvntFHRHBRTD1HSszXndGSnLTdmtuacjuy05cbM1pyr16ePP0Fw5Ei45prCZsfWLPYARERERKR0dO8OL7zgr6rxxRfw179CkxQchlVPs4iIiIjkbO5c2HdfWG89uPVWaNEi9ohyo55mEREREWl0HTv6j93+4QcYMAAWLIg9osalojko9h6iJGVrzunITltuzGzNOR3ZacuNma05192KK/qTAn/zG38t55kzC5ddaCqaRURERKTemjaFf/wDBg2CXr1g2rTYI2oc6mkWERERkby4/XYYNQoefNAX0MVMPc0iIiIiEsWQIb5w3n9/eOSR2KPJLxXNQan1EJVytuacjuy05cbM1pzTkZ223JjZmnPD9OsHY8fCiSfCddcVNrsx6TrNIiIiIpJX22wDzz9feS3niy4Cq3MjRHFST7OIiIjI/7d3puGSFFUafg/Nvjf7yA6yDYggsuOAII44KAiiNDA0oyyDzAgoCiiKyrC5jQiogCDoCAoCKo6yNziIrM0OgggNIpuCQAsq0HzzI6K6s4uqe2/fW5GnbuV5n6efrsysW1/GyYzIkxEnTgRF+NOfYMcdYa214Nvfhnnm8T6jWURMcxAEQRAEQdAXLLUUXH01/PnPyXmePt37jEZPOM2ZQYghGi/aUeZmaDdN11M7ytwM7abpempHmXvLggvCRRfByivDNtvAk0/Wp91LxuQ0m9lEM7vczO43s8vMbLEO31nJzG4xs6lmdpeZHTAWzSAIgiAIgmB8MffccNppsPPOsMUW8MAD3mc054wpptnMTgSekfRFMzscmCjpiLbvzJ11XjGzBYF7gM0lPdnh9yKmOQiCIAiCYIA56yz41KdSSrrNNvM7j7pjmncCzsmfzwF2bv+CpFclvZI3FwDG+dzJIAiCIAiCYLR86EPJcX7Pe+CnP/U+m5EzVqd5GUlPAeSe46U7fcnMVjCzO4BHgBM79TJ7M4gxRP2qHWVuhnbTdD21o8zN0G6arqd2lLk87343/PznsO++cMYZ9WqPlmGdZjO7wszurPy7K///3pGKSHpM0puBNwL7mFlH5zoIgiAIgiBoBhtvDJ/7HJxxhveZjIyxxjTfB2wj6SkzWw6YImmdYf7mLOBnki7qcEyTJ09mlVVWAWDxxRdngw02YJtttgFmvQXFdmzHdmzHdmzHdmzH9vjffvllWGWVa/jkJ+GQQ8rqtT5PmzYNgHPOOWeOYpp7MRHwWUknDjERcHnSZMG/mdlE4AZgF0n3dPi9mAgYBEEQBEHQIM49F04+Ga6/vt5VA+ueCHgisL2Z3Q+8Azghn8RGZnZ6/s46wI1mdhswBfhiJ4fZm+pbSBN0PbWjzM3Qbpqup3aUuRnaTdP11I4y18tyy13DSy/1/6TAucfyx5KeJTnL7ftvBfbPn68E3jwWnSAIgiAIgmAwmWsuOP54+MQn0qqBEyZ4n1FnxhSe0WsiPCMIgiAIgqB5SLD11vDhD8PkyfVozml4RjjNQRAEQRAEgTu/+hXsuSfcfz/MN195vbpjmgeGJsYQRZkHX9dTu2m6ntpR5mZoN03XUzvK7KO95Zaw/vrwrW+5ncqQhNMcBEEQBEEQ9AXHHpvim6dP9z6T1xPhGUEQBEEQBEHfsPfesPrqcPTRZXUipjkIgiAIgiAYtzz8cFot8L77YOmCa0hHTPMoaXIMUVN0PbWjzIOv66kdZW6GdtN0PbWjzL7aq64Ke+wBxx3ncz7dCKc5CIIgCIIg6Cs+/Wn47nfhkUe8z2QWEZ4RBEEQBEEQ9B2f+Qw89hh85ztlfj9imoMgCIIgCIJxz/PPwxprwJQpsO66vf/9iGkeJRFDNPi6ntpR5sHX9dSOMjdDu2m6ntpR5v7QXmwxOPxwOOqoes+nG+E0B0EQBEEQBH3JRz4Ct9wCN9zgfSYRnhEEQRAEQRD0MWeeCd/7XgrTsBEHUwxPhGcEQRAEQRAEA8PkyfDkk3D55b7nEU5zJmKIBl/XUzvKPPi6ntpR5mZoN03XUzvK3F/ac8+dltc+8kh47bV6zqkT4TQHQRAEQRAEfc0uu8CECXDBBX7nEDHNQRAEQRAEQd9z1VVw4IFwzz0wzzxj/72IaQ6CIAiCIAgGju22g5VXLrfYyXCE05yJGKLB1/XUjjIPvq6ndpS5GdpN0/XUjjL3r/Zxx8EXvgAvvVTufLoRTnMQBEEQBEEwLth4Y9h8czjllPq1I6Y5CIIgCIIgGDfcfz9stRU88ABMnDj634mY5iAIgiAIgmBgWWst2Hln+NKX6tUNpzkTMUSDr+upHWUefF1P7ShzM7SbpuupHWXuf+2jj4bTToMnnuj9+XQjnOYgCIIgCIJgXLHCCvChD8Exx9SnGTHNQRAEQRAEwbjjmWdSqMYNN8Ab3zjnfx8xzUEQBEEQBMHAs+SScOih8NnP1qMXTnMmYogGX9dTO8o8+Lqe2lHmZmg3TddTO8o8frQPPhimTIHbb+/d+XQjnOYgCIIgCIJgXLLwwvDpT8OnPlVeK2KagyAIgiAIgnHLyy/D2mun5bW33nrkfxcxzUEQBEEQBEFjmHfetLT2kUdCyb7XcJozEUM0+Lqe2lHmwdf11I4yN0O7abqe2lHm8ac9aRL85S9wySVjP59uhNMcBEEQBEEQjGsmTIDjjkuxzTNmlNGImOYgCIIgCIJg3CPB294G++8Pe+89/PfnNKY5nOYgCIIgCIJgILjuOthrL7j/fphvvqG/GxMBR0nEEA2+rqd2lHnwdT21o8zN0G6arqd2lHn8am+1Fay3Hpx2Ws9+cibhNAdBEARBEAQDw3HHpX/Tp/f2dyM8IwiCIAiCIBgo9toL1lxz6CW2I6Y5CIIgCIIgaDQPPQSbbAK/+Q0stVTn70RM8yiJGKLB1/XUjjIPvq6ndpS5GdpN0/XUjjKPf+3VVoPdd4fjj+/db4bTHARBEARBEAwcRx0FZ58Njz7am9+L8IwgCIIgCIJgIDnqKHjiCTjzzNcfqzWm2cwmAj8EVgamAR+Q9HyX7y4C3AdcJOmjXb4TTnMQBEEQBEHQE55/HtZYA669FtZZZ/Zjdcc0HwFcKWkt4GrgyCG+ewxwzRj1ihExRIOv66kdZR58XU/tKHMztJum66kdZR4c7cUWg09+MvU4j5WxOs07Aefkz+cAO3f6kpltBCwDXD5GvSAIgiAIgiAYMQcdBDfdlP6NhbGGZzwraYnK9jOSlmz7jpF6ofcC3gFsFOEZQRAEQRAEQV18+9tw7rlw1VVgOSBjTsMz5h7uC2Z2BbBsdRcgYKQd3R8B/lfSH5L/zJAnt88++7DKKqsAsPjii7PBBhuwzTbbALO672M7tmM7tmM7tmM7tmM7tke6veqq8Nvfwt57X8OECdMYFZJG/Y80sW/Z/Hk54L4O3/kf0iTBh4A/As8Bx3X5PXkxZcqURul6akeZm6HdNF1P7ShzM7SbpuupHWUeTO3zz5c22kiaMSNtZ79zxH7vXKNztWfyU2Cf/Hky8JMOTvleklaRtBpwGPBdSZ8ao24QBEEQBEEQjJhdd03/X3jh6P5+rDHNSwDnAysCjwK7SXouT/w7QNL+bd+fTMQ0B0EQBEEQBA5ccUWaGHjPPTDvvDXmae414TQHQRAEQRAEpZDgHe9IS2zvv3+9eZoHhlbQeFN0PbWjzM3Qbpqup3aUuRnaTdP11I4yD662GRx/PHz+83P+t+E0B0EQBEEQBI1hk01g003n/O8iPCMIgiAIgiBoFA8/DKutFjHNQRAEQRAEQTAkc7q4SYRnZCKGaPB1PbWjzIOv66kdZW6GdtN0PbWjzM3RnhPCaQ6CIAiCIAiCYYjwjCAIgiAIgqBxRHhGEARBEARBEPSYcJozEUM0+Lqe2lHmwdf11I4yN0O7abqe2lHm5mjPCeE0B0EQBEEQBMEwRExzEARBEARB0DgipjkIgiAIgiAIekw4zZmIIRp8XU/tKPPg63pqR5mbod00XU/tKHNztOeEcJqDIAiCIAiCYBgipjkIgiAIgiBoHBHTHARBEARBEAQ9JpzmTMQQDb6up3aUefB1PbWjzM3Qbpqup3aUuTnac0I4zUEQBEEQBEEwDBHTHARBEARBEDSOiGkOgiAIgiAIgh4TTnMmYogGX9dTO8o8+Lqe2lHmZmg3TddTO8rcHO05IZzmIAiCIAiCIBiGiGkOgiAIgiAIGkfENAdBEARBEARBjwmnORMxRIOv66kdZR58XU/tKHMztJum66kdZW6O9pwQTnMQBEEQBEEQDEPENAdBEARBEASNI2KagyAIgiAIgqDHhNOciRiiwdf11I4yD76up3aUuRnaTdP11I4yN0d7TginOQiCIAiCIAiGIWKagyAIgiAIgsYRMc1BEARBEARB0GPCac5EDNHg63pqR5kHX9dTO8rcDO2m6XpqR5mboz0nhNMcBEEQBEEQBMMQMc1BEARBEARB44iY5iAIgiAIgiDoMeE0ZyKGaPB1PbWjzIOv66kdZW6GdtN0PbWjzM3RnhPCaQ6CIAiCIAiCYYiY5iAIgiAIgqBxRExzEARBEARBEPSYcJozEUM0+Lqe2lHmwdf11I4yN0O7abqe2lHm5mjPCWNyms1sopldbmb3m9llZrZYl+/NMLOpZnabmf14LJqluP322xul66kdZW6GdtN0PbWjzM3Qbpqup3aUuTnac8JYe5qPAK6UtBZwNXBkl++9KOktkjaUtPMYNYvw3HPPNUrXUzvK3Aztpul6akeZm6HdNF1P7Shzc7TnhLE6zTsB5+TP5wDdHOIRB1kHQRAEQRAEQb8xVqd5GUlPAUh6Eli6y/fmM7ObzOx6M9tpjJpFmDZtWqN0PbWjzM3Qbpqup3aUuRnaTdP11I4yN0d7Thg25ZyZXQEsW90FCDgKOFvSEpXvPiNpyQ6/sZykJ81sVVIYx7aSHu7wvcg3FwRBEARBENTCnKScm3sEP7Z9t2Nm9pSZLSvpKTNbDni6y288mf9/2MyuATYEXuc0z8mJB0EQBEEQBEFdjDU846fAPvnzZOAn7V8ws8XNbN78eSlgC+DeMeoGQRAEQRAEQW2MaUVAM1sCOB9YEXgU2E3Sc2a2EXCApP3NbHPgNGAGyUn/b0lnj/nMgyAIgiAIgqAm+moZ7SAIgiAIgiDoR2JFwCAIgiAIZsPMDh7JviBoEn3hNJvZfCPZNyiY2Ykj2ReMHTPbbST7Bo2cqWbYfYOE17Vumq2965SHvT0dSDPbciT7CjC5w759SouGs14/XveYY5s9ah+sL5xm4Ncj3NdTzOyqkewrQKeMJDvUoIuZfW8k+wZIu9Mqld1Wruw5jva+sMO+H5UUdKxPLbyude22buFkc9c6hY+9XRzIzMkj3NcTzGySmV0CrGpmP638mwI8U0q3gputvdprz+dyptZ7rIJXWzJqH2zYlHMlyWnqlgcWMLMNmbVy4KLAggV158+/v5SZTWzTfUNB3QOBjwCrmdmdlUOLAL8qpdvGum3nNAHYaNC0zWwH4N3A8mb29cqhRYFXS2h2oVZ7m9naWXMxM9ulcmhRYP5Cmi71qaLvcq09bF3Rrt3m3nXK6d6eBOxBdiArhxahsAOZJ9FvASxtZh+rHFoUmFBQ+nrgCWAp4CuV/dOBOzv+RQ/wtHUFr+eji67XPebYZo/ZB3N1moF/Jr1BrgB8tbL/BeBTBXUPAA4hPVymtumeWlD3XOAXwPHAEZX90yU9W1AXMzuSZNMFzOwFZj1kXwZOH0Dtx4FbgPcCt1b2TwcOLaQ5E0d7rwXsCCwOvKeyfzqwXyFNr/rUwutae9i6hYfNXesUPvZ2cSAz8wILk57Ti1T2vwC8v5SopEeAR4DNzWxlYA1JV5rZAsACpLKXwM3WXu2153M543KP4deWjNkH64vsGWa2q6ROQ26ldf9TUh1DEJ20tyI1Rt/J+asX6bRKYgHd4yXVOZTqqm1m85AahJUk3V+ndtZ3sbeZbS6peIhTm6Zbfcr6Ltfaw9YV7dpt3gd1ysXeHRzIuSWVciBn05X0iJktJOnF0noV3f2A/YElJK1uZmsA35K0XQ3aXrb2aq/dnstZ3+sec2tLRuuD9UtM86/M7Ewz+wWAmf2jmX24Bt2zzOwoMzs9665hZjuWFjWzo4HDmRW7My/wP6V1M582s73M7DP5XFY0s00GWPtdwO3ApVlzg7ahv9J42fsZM7vKzO7Ouuub2VGFNV3qUwWva+1h6xYeNveuU7XbOzuQPyKtOQBpdPTHJTUrvMHM7gXuy+fyZjP7Rg26BwFbknodkfRbYJnSos629mqvPZ/L4HePubQlY/HB+sVp/g5wGbNi8R4gDT2W5izSMMgWefsx4L9q0H0faVjiRQBJjzP70EhJTgU2J8WOAfyFeobQvbQ/B2wCPAcg6XZglcKaVbzsfQapQXgFQNKdwO6FNb3qU4vP4XOtPWzdwsPmn8O3TnnY28WBzHyNFMr4TNa+A/inGnT/Lunl1oaZzQ3UMTTtaWuv9trzuQx+99jn8GlLRu2D9YvTvJSk84HXACS9SlpBsDSrS/oisxrfvzIrpqgkLyvFxQjAzBaqQbPFppIOAv4GIOnPpLesQdV+VdLzhTWGwsveC0q6qW1f6claXvWphde19rB1Cw+be9cpD3t7OZAASPp92646no/Xmlkr3nZ74ALgkhp0PW3t1V57PpfJmh73mFdbMmofrF+c5hfNbElmFWAzoA5DvpzjpVq6qwN/r0H3fDM7DVg8D0VdSeo9qYNXLM3MbZV5afLLyoBq321mewAT8tD1yaQJJ3XhZe8/5fu5pft+0iSbknjVpxZe19rD1i08bO5dpzzs7eVAAvzezLYAZGbzmtlh5GH0whwB/BG4izTx9OdAHWFHnrb2aq89n8vgd495tSWj98Ekuf8D3kJK9/F8/v8BYP0adLcHriU1DN8HpgHb1FTm7YEvAV8Gtq/R1nsCPyUN4x4L3A/sNqjapLRcxwI353//Bcw/6PYGVssNwUvAH4DrgJULa7rVJ89r7WFrT5v3QZ3yuLfnImXouIAUb7sfeSJ9DeVdKl/bp4CnSbGXSxbWnAB8v65r2ke29mqv3Z7LXvdY1nVrSxilD+aePcPM5gI2A24ipRQy4H5JrxTWNdIEg5eyvgE3SPpTYd0JwJWS3l5SZ5hzWBvYjlTmqyTV8UZZu3a29YmSDiulMcLzqNXeuU69X9L5edhpLhWeee5Vnyr6Ltfaw9YV7dpt7l2nnO7tCcB3Je1ZUmcI7Y9K+m8H7euAbVUJlahB083WlXNweT466rrcY45t9ph8MHenGcDMbpO0oYPuXZLe5KB7FbCLao7lyQ+ceyWtXaeup7aZ3SBpszo1K9qe9r5F0ltr1nSpTxV9l2vtYeuKdu0296xTWd/j3q7dgaxo3yxpYwfd7wLrkHpAZ6Yhk/TVrn/UG10XWzs+o9yeE5Vz8LrHvNrsUftg3oubtLjKzHYFLlK9XvxUM9tY0s01akKaGXuXmV3B7I3RR0uKSnrNzO43s5UkPVpSq4+0b8spbC5gdltfVFrY097AlTku7YfMXu6Si+h41acWXtfaw9YtPGzuVqcyHvZ+iJQatVYHMnOdmZ3C68s7tfuf9ITf5X9zUV92J3CytVd77fycaOF1j3m1JaP2wfqlp3k6sBBpBvTfSMMTkrRoYd3fAG8krX70YkV3/cK6kzvtl3ROSd2s/UtgQ1I4TPVmee8gapvZdzrslqQPldJs03ext5l1StIuSasV1HSpTxV9l2vtYeuKdu0274M65XFvH91pv6TPl9KsaE/pLK1tS2sPhZmdLOk/C/yup6292mu353LWd7nHHNvsUftgfeE0D4eZrSvpngK/u3Kn/UrLiGJmE5VSv9SKmV0oaddCv711p/2Sri2h1y/a3TCzIyUdX/D3+67MAGa2vaQrevybfVmfWpS+1kPo9tzWld/uO5t72bmiX8zeQ2gWcSBHqD25jg6XDrpTJb3FQbeYrb3a6359TrRwvMe82uyuPth4cZq9KqeXrkuMd9b+taTNm6LtdY0r+i729ih3H9i6Ue2Il3YTr3PTrnETdbO2V3vt9lzO+o261kP5YP2Sp3k46lwgoR90Pd9k5m+Yttc1buFlb49ye9u6ae2Il3ZTr7MXTSuvJ17ttedzGZrXdnb1wcaL0+zlRPZ/N3zv8Syzh7b3NW7SvR22boZ2U6+zF17lbaKz3sQ2xFPfu9yvY7w4zU2jiY2RF2Hr5hDXuh6aaOemjSYAnOSk28T7y5um9TR31e2XlHPDUXtuzIzXBTvcSRea1/hf4KBZxcve0xw0vR92Xtd6mpMuNLNOTXPQ9HIgIa2i23PM7BJe39P3PHALcJqks0vojgBPW/edE1cTRe6xEeDVlnT1wfpiIqCZdQr0fh54RNKrBXWX6LB7uvJqhGa2RIncn2Z2F90bo/+S9EyvNUeKma0n6e5B0Tazr3fY/Txwi6Sf9FJrNJSyt5nt0mH388Bdkp7utV7WdKlPFX2Xa+1h64p27Tb3rlNO9/ZwDuTfSuhm7Y912P08cKuk2wvqngQsDZyXd30QeIFkh0Ul/WshXTdbD4fX87G0ruM95tVmj9oH6xen+QbgLcCdpDeq9YB7gMWBf5d0eSHdacCKwJ+z7uLAk6T11/eTdGsh3S8CM4Bz867dSWuwPwlsJek9JXSz9nS63ywfl/TQIGmb2enA2sx6Y90VeBhYEnhI0iG91mzTd7G3mf0vsDnQyr+5DXArsCrwBUnfK6A5DYf6VNF3udYetq5oT6Nmm/dBnfK4t10cyKx9LvBW4JK8a0fSs3IV4AJJXyyk+7pV4lr7zOweSesW0vW0tVd77fZczvpe95hXmz16H0yS+z/gImDdyvY/Aj8CVgNuL6h7BvDPle13AqcBmwE3FtSd2m0fqbekpK2PAQ4grfC0KLA/cCKpYbpm0LSBG4AJle25gV8DE0hLlxYrr6e9gcuAZSvby+Z9SwB3F9J0qU/e19rD1p4274M65XFv39xtH3BP4fL+Eli4sr0wcC2wQOH7+j5gpcr2SsB9+fNtBXU9be3VXrs9l53v13AVigAAIABJREFUMa82e9Q+WL9MBFxTlcVLJN0LrK3Cb1fAZpIuq+heDmwu6QZgvoK6E8xs09aGmW1CukkgrYpYkvdKOk3SdEkvSDqd9ND9ITBxALUnkhqAFgsBS0iaAfy9kGYVL3uvKOmpyvbTed+zwCuFNL3qUwuva+1h6xYeNveuUx72XtjMVmpt5M8tG5Sec7NMm8YrpJeGv1LW3h8nLa88xcyuAf4POMzMFgJKLnThaWuv9trzuQx+95hXWzJqH6xfJgLeY2bfBH6Qtz8I3Gtm81H2ofOEmR3epvuUmU0AXiuouy9wlpktTBpSfQH4cG6MSq9+85KZfYDUkw/wftLS5VA+vYuH9heB23Ojb8A/AcdlW19ZSLOKl72vMbOfMWvY6/1530LAc4U0vepTC69r7WHrFh42965THvZuOZC/I5V5VeAjNTiQAN8HbjCzn2TtHYFzs/a9pUQl/dzM1iANnxvwG82KJ/5aKV18be3VXns+l8HpHsOvLRm1D9YvMc0LAB8BtiIV4DrgG6SbZkFJfymkuxRwdJvu50mxRCtJerCEbkV/MdI1KP1grWquRpp93Fpd6NfAocAfgI0kXTdo2mb2D8AmpGt8k6THS+h00fYqswG7MPu9faEKVnjv+pTPofZr7WHriraLzZ3rlIu9cydOJweyOGa2EZXySrqlBs15gANJjgzANaSJeKVHT9xs7dheuz2XK+dQ+z2WdT3bkjn2wfrCaQYws3mBtUhvVffXUTEr2osCr5VyzjvoLUZ60LUao2tJE1ier0O/aZjZe6nYWtIlQ31/UDCzZUmNkUiNUdFMDhXdWutTm7bLtfaydUW/7jbMtU7VbW9PBzLrvzlrC/g/SXfUoPltYB5m9e7+KzBD0r6FdV1t3VQ87rGsW3tbMhYfrC9ims1sG+C3wCmkHuYHzOyfhvyj3ui+ycxuA+4ihYjcambrldYFzgKmAx/I/14AvlODLma2gpldbGZPm9lTZnahma0wqNpmdgJwMGmI6V7go2ZWOgSmqu9i7zzUdxNpmO8DwI1m9v7Cml71qaXvcq09bF3Rrt3mfVCnPOz9TWAj0vPpG/nzNwtrAmBmB5OGz5cixZ7+j5n9Zw3SG0uaLOnq/O/fgI2H/aux42lrr/ba7bmc9V3uMce2ZPQ+2GhnH/byHyld0FqV7TVJ+QFL614PvL2yvQ1wfQ26r8sI0mlfIe0rgH8jxbPPDewDXDGo2qS0OXNVticAd9ZRXk97A3cAy1S2lwbuKKzpUp+8r7WHrT1t3gd1yuPeft3v13iN7wQWqmwvVNN9PRVYvbK9Gh2yDgyYrb3aa7fnctb3use82uxR+2B90dMMzCPp/taGpAdIw0KlWUhSK9cnkq4h3Syl+auZbdXaMLMtgb/WoAuwtKTvSHo1/zub9NAZZO3FK58Xq0GvileZ59LsQ9bPUH5kyas+VfG41h62buFlc8865WHvGWa2emsjx6DOKKw5U65Na0beV5pPAFPM7Bozuxa4mjRJrzSetvZqrz2fy+B3j4FPWzJqH6xfsmfcYmZnAq2k9HuSep9L85CZfaaiuxcpsXZpDgTOaQWhA8+S3izr4E9mthezEsdPIj10BlX7eOA2M5vCrNm5RxbWrOJl70vN7DJmXyDg54U1vepTC69r7WHrFh42965THvZuOZAPkcq8MqlnsA6+QwpBuThv7wycWVpU0lWWsmesxawJeXWkFPS0tVd77flcBqd7DL+2ZNQ+WF9MBMwzZQ9i1szNXwLfKF1BzWwiaaZ5Vfdzkv5cUreivyiApBfq0MuaK5FixzcnBfxfDxws6ZFB1c6zczcmXeMbJT1ZUq9N29PeuwJbku9tSRcP8ydj1XOtT/kcXK513bau6LrY3LNOZf3a7Z2fU3U7kC3tt1C5xpJuK6jVaZnymUi6qJR25RxcbO34jHJ7TlTOobZ7rE3X8/k8xz5YXzjNTcE6r+8+E0lfretcBp3cAHRF0tS6ziUoS1zremiinT0dSDNbYhjtZwvpDjUhSpI+VEjX3VlvGo73mEtb0gsfzDU8w8zuYojE3ZLWL6R7yTC67y2hS1oi0wUzO5mhy/zRAdP+yhDHBGxbQHMmXvY2s+lddC3JatECml71qYXLtfawdUXbw+bedcrD3u8Z4piAko7crVmjFVvaKrvlz6uVEFXKkjEsZjZZUi8XG3GztWN77fZczrjcY/i1JWP2wVx7ms1s5aGOlxqaMLOth9G9toTuSDGzIyX1NO2KmU0e6niPG7++0R4OM9te0hUFfrdvywxpWL9XQ/j9Xp9alLrWI9Dtma0rv9m3Nveyc0W/5/YegWavHcg50V5X0j0OulMlDdljWEi357b2aq/7/TnRwvEe82qzu/pg4yI8w8x+LWnz4b/Zc90LJe3qoOvSGGXtkyXVkQO0L7Q9bZ31XeztUW6v+lTR93rIe9bn2m3eB3XK4972vMZe9/VtkjZ00G3c89HzuZz1G9V2DqXbLynnhmN+J91SQxPDUVeql05s2TBtT1uDn709yu1Vn1p4XWvPe8zD5t51ykPfs8xe2l49bk18Pno+l6F5bWdX3fHiNHtVzqbpNpGm2tqj3N62bmJ9jus8uJre2k1z1ptM09rOrrrjxWluGt49NUEQBMHIaWKb/Ssn3SbaOqiXrvdYvyxuMhx910XfcyGzQyR9LW9eUJdup1MZdG0z21TSjXlzWh2aQzDw97azZpVpTroDX6famOagWaVnZTazXUaY6szLgQR4uZc/ZmZ7D3Vc0nfz///RY93FJD3f5djGkm7Om562blJ7XaWn99gcMM1Jt6sPNl4mAq4n6e4Cvzs/8EZSV/zvJP2t7fg7JV3ea90u5/KopJXq0BrmPPbJS3gOrHYdtjaz4yR9agTfK15mM1sIeB8wSdK/5H1L9CoHp5kt2i05vJmtJOnR/LlIfcoraZmk77Xt/1dghqRze62Zf39EOU57aes5pdc2N7NlSAtRrUtqN+8lLUT1VK80htBut7eA59T2EOvxve06qbEdS8tLTwJ2l7ReIY2TO+0mpYRbXlKRzjYzuwXYvj3ziZm9EzhT0ooldCs6S5NWH3xQ0nNdvuPyfCyla2a7Srqww/55gcMlHdNrzfz7Ljm5e5HizzvlXHveTQF/AqaQLliRZSTNbG7gOOBDwCOkMJUVSEtJflrSKyV0hzmn35dsFDxz6ZrZUqQH7Z+Bs4AvAW8Dfgd8XNKDpbS7nE9RW2cN7wwC8wLvBvYA3gVcCFwk6ZICWjPLamZXSdqu07FSmNmNwHaS/tK2fyHSylYbFdJ9DXgMeLW1q3JYkopNwuuQ477adn65vQOgR5pbAucCZ5PyuxrwFmAysKekoj2AZvYws+eUBVgYuAPYV9K0ApruTrOlFdM+SKrL65OWHr5I0l01aBuwJ3A46QXpWEl3FtLaj/Sc2F7SH/O+PYBjgZ1K6WadfUk+we+AVYH9Jf20lF5F1zXHvaXl6F8DPiLp4bxvB+C/gUslHVJI12sBnTGn+Ou7nmZLy8LuA2whabdCGv9NSnJ9qKTped+iwJeBv0o6uITuMOdUtPezktfVgDOAfavHS+Z1NbPLgVtINt+O9HJyCclx3lPSNqW0u5xPHT3NdwDb0GVYrVTPo5ltT+qJ+meSA/VD4GRJq5TQy5ozU0+1p6GqIy3VUI6Nmd2pcosknUS6xr8CzgOua+/1LIV1znG/BMmBXUjSfgU0bwAOVNvyuma2AXCapE17rTnC89qF5OS8q8BvvwR0eqlvLahS5N7K2vuR6vIKwPn5308krVpKs6I9N+k5/HHgRuB4SffXoPuvwCeBd5JeFP4deFeJF6I23buBt0v6o5mtBny/jjS31gf51s1sEvBfpBfi9YClgYMk3VFaezzSd05zi5Jv+Gb2W2DNDsN6E0jr3K9RSHeoFa0WKDXs1eE8as2vaWZ3SHpz7rV4pOqwmtntkjYooNntDd6AbSUt1GvNNv2/A3+gs9NcrBcy937+H7BPpefgocK9ntWe5tnqbU09zfcBb5X0Ytv+RYCbJa1dUNtIjvMkYBPgcuCbLdt7UKp+m9m9kv5xTo/VQan7zMzuIY3YdESFFuDK2i8DvyaNxt2S9xWty1njIOBg4CrghJJl7KK/G3Ay8CiwQ6kR5zbN2tutrHMY8ENJvy+tNcQ5TAA+DxwCPEd6Pj5QWHNT4HRgdeAu4EOS7iupWdGeTLq/18q77gO+rhyrPxx9ORHQzOah7LmpU4+QpBlmVuwtQpLbMtpt1P2mNAOS0c3sT23HXiuk+eVRHusV99b5YlJhI2B34Eozewj4ATChsOYyZvYx0gtC6zN5e+nC2gBnAj8yswNbPVJmtgpwaj5WjNyOTDGz20h2Pwb4LWk0x4tSWZHMOqy2l2ON3TIxmdnCBfVfrttprPAGYDfgq2a2LKmneZ4adE8Gnga2Ai5J74VA4d71SsiRAQsCS5LqVvFefWAFM/t6t+2RxLqOkuWB63Po0XnABZLan5HFMLOtgG+QRstWBLYmXfMfkkJx/l5I+lTgMOCXwHuBr5FGR4tiaZLrIcDHgKnMCjH7kpkxEsfZO6a5UzD4RNKwzHWSvlBI98ekmLDvtu3fC/hA6Tiiit7yzHJoHpf06lDfH6NWdRLNFNpCB0qFC2Tt50iVw0ghGb9sHQK2kjSxkO6GpDfZe+p6i61ou6yW1XYOW5J6QHcFbgculnR6AZ2jhzou6fO91uxwDv8OHEmKcRXwIqmX7JsFNRcCdiK1V0sDF1FTr5GZdeoFmwjsBfxFBVYPM7P9gf1ID7upefdGwInAWZJO67Vmm/7HOuyeSHroniKp5y8qZnaKumSJMLNlVcMEyKy1AumlbBLJobxYI5hoPEqtTqE/Myn1EuGlm7XdlrPOLwX/RLq+O5Fi9M8jXePppXSz9i2keOabKvsWBI4mxZEXGaVz7Nm/gTSJdlrb/lWAH0jabNjfcHaa24PBBTwDXCPpfwvqLk96wP2VNKFFwMbAAsD7JP2hkO6RwDytlwEzexR4ntR7cI66rHXeI+1Ok2haFAsXyNq1x22Z2WdJDsStwKakmLzaev/MbP8SDupoMLO5gO1JjcW/eZ9Pr7FKWrDc62ilHzZZ60VSr/J5pLjX2RpTFZoBnrWntO2a2XYCp6vQZGYz25EUc7pu3nUP8CUVmGDaQbv95axV5l+qhklx+RwWI72E7gGsI2n5OnTbzmEt4IMFO5VW7PbiZ2Zvk/R/JXQ7aC1JciYflXRrHZre5FCJdwAnAGtJWrCw3lySOo72mtk6pTqb8ijoYZVdX65ul2o7exFi1rcxzXVgZtuSGn8j9UZeVVhvKvC2VuxlqzcyV5RrJW1VUt8Lq6Qdq1HzHmBjSS/lxvdSSRvXqF+N8z25RM/fENpzAzsArV6C+0jlLzKSYSl14wdJ2VEuAT5Betj9Djim9HBjXb0UHXTPpnuok1RoBnjW7pgqKug9ZrYAqTd7D9JQ7iLAziRnvVR4GWb2SUlfzJ93k3RB5diIUlqOUvch4FvAV1ttRg4P+QrJkSvSjprZz4AjJN1tKWPIVNIE8tVJL4JfG/IHxq4/pljXHui/idTb/EHSy+B5NZTZK13n2Ti0nWZ2q7pkUxrq2Gzf83aazeztwH8w+wP+FEnXFNTcGFhK0i/a9u8APF3qrbbDkMTM3IsjvWBj0F6ZlNP0+bz9dlLDPw04VVKx5OVtDuSFknYtpVXRnM2epe3bQb+aUaI2p87M3kAKv3kCuI30QrghsBywjaQnCmieD7wCLEQaLr+b5DxvBWwgacdea7bpu6cFqxuPMpvZusDqyqm4LGUhWiwfPkXS1K5/3Bv9IVOAlQirM7Pvk14ALyfND7ialMe3jgwWLhNsLWWwOgHYguREvokUA/pF0iTXIi8KZnaPpHXz508Ba0va29KE3l+VjGnOsa6H0iHWFTiplONsZmswK+xmBukeO0/SQyX0Oui7pOv0wobOhrOaRpAgwHUioJn9C3AK8IX8r3WjnmVm/yHp54WkTwQ6DVPfS0qHtm0h3YXNbJ7W0GnFYZ4PWLSQZovzSQtcPG8pRdQFpHyfG5AmAuw7xN+OlWpISNGZ3xVWrzxkrW27eP5L6p9s2eI40oNtth4KM/so6UE4ZOzeKPlHSevlHu7HJLXCcS61lHqvNGubWaccrnWkBVuP1LNeXezjy3WFC9TMCaQ2o8U/A58hxdh+lvQSXpLNgd+TwmFupHOoWa9ZjzSCch8ps1LRyeJtWJfPnbZ7htJEzwPM7GDgSuBxYDNJj5XSzFRDirYjT6aVNN1SVqCSfIQUmjmtsu9qM9uV5MiW6m2+jHQ/f9CpzZin3WEGkPSipYQMRbC0yMin2sPozGxt0gv4OwpJr9PpdEhpHUc0cuOdPeMTwM6aPR/g7Tk4/WSglNO8SKdJBZIeyUP5pfgRcFp+IXgJZr7RnZKPlWQBSY/nz3uRJu58Jce73l5Yu30RhjrYqW27jowZVVqOXMthbzl1pR25zSTt075T0tfNrFSe1Zezxqtm9njbsRmFNKs8TFqtrFbMbCfSfXU8aejaSBPjLjKzwyT9pKC8x4vCP0i6vrL9QitExMwOKKDXznKk2PxJpFCJ/yX1yt1TSlApVebaWe9KM3saWMTMlpP0ZCndlnyXz522e4aZLU7qWNqUtDDSu4FfmNnBkq4upQv83sz+k7Rg0FuAS/P5LED5rCGLqkMuaEnTLK3hUIrfSfp0wd8fjgXMbCF1Ttc5b0HdJ0m+3mcknWtp8uHnSC/eh5cSrfp9ufNwD+ADpGfIiMLdvJ3m5dQhgbakO3MMVSmGytZQMn/vZ0irGz1qZo+QHnArktJifaagLszeM7EtKdMAkl4zK95h82YzeyGfwwL5c+ucJKnnjZK6TC40sxVJw2Glk8Z3eqOtg78OceylQpqt9EzG7KmajJRSqTReacG+QFq9bFpl3x1mdjXwk/yvFB4vCrOlzNTsM82XKS0uaQbJkbo0j85NAq4xsy9I6rT0c690f0PqSf+smb2V9KC9ycwek7RFKV2GbjfnL6h7K/BN0gIXrwKXZwfjG2b2iKRJhXQ/TKpT7yD1vLaWst6MNAJckqHazaGOjZWSnXQjwSVdp6Rjzexc4BRLmY/eQBoN36DVoVgCM1uTWeEwz5AW/zJJbx/pb3g7zS+O8thYudLMjgWOUiWo28w+T4pZK0Ju9I/IOm/Mux+UVLJStpiSY0+fIL00XA2QJ1wUi2cGkFQ6T/CQWFrGezdSRVkeuLi0ZrsTZ/XNBF/MOqdyNMqFAH2i8vmWtmPt2yUounzzEMwzRO9U6Z4xjxeFx81sU0k3Vnea2WakIfziZGf5X0h1eRXg66RMSLWgtMjILWb2cVJ9Lqnl1W5K0pfbdtwObGFplcJSok+TVgBs3z+FNE+jJOsMMXJTMqRw8S7tNVA2A0/+/S+b2V+Aay1lHqolXWdLPv8/NynP+n0lHebMb0iLf71H0oMAZnbonPyAt9M8W5xphdI36seBbwMPmlkrNGED4GbKxvZiZssAB1GJgTSzU3ODUZKDSbNy/4GUG7kVP7Yc4Dk8VIQ8vPQ+Uq/QmiRHeTVJK9Sk33UmuJmVnAl+Ld17IH/ZZf+YUMEcpiPk5jyRpyOlJvEAr1iHzDB50m2xnOsZjxeFw4EfWpr5Xs3TPJnUthTFzM4hxRj/Avi8pLtr0Pz6MF8pvsyxAy90O6DCaTstZbD4KLMnBqgjg8WYY11HyWLAjnSOURc1vBBK+hbwLas3XedRpGXaPy3ph5bSAJ9kZvsCB0q6t5D0rqSe5ilmdikpXn2Ohtq98zS7rrtuaY35mflGS89YtbTYxLnA2aQhsNbEx8nAnpKKPQjN7HJJ7yz1+/2Gmf0VuAk4irRQjqyGJWgr+i4zwa3GBRfadN3SNeVJJa/bTXp5WF6Flqc3s51JGQWOY/Z870cAh0v6cQndrD2ZIeJaC872X4aU7aiap/nUOu65PBmsNQJZLXuxMC9LS1nfTRo6fpy2B2wfvDD2HDN7DPhqt+OSuh4bo65LBosO5/G6WFdJpxTScs/8Yynl7UTl1KBmNi/JoT1UUpEwQzM7iTTS3z4RcAdSqsOi4Y15LtnOpBGrbYFzSIvJXD7s33o6zV5Y9/Rrj5BmbhYJV7C0Gs2Bkm5r278BcJqkTUvoZg33FerqJA+57E6KUT+XFLt0RY1O8+2SNsifrwLOkPSD9mMFdJ8E7iLNyL6wdY+XpF8edvlcDNiT1Ct6L2kp2E7Drr3SezNp5KqV7/1u4Cud5mr0WLf2FwUzO4y0albpLAp9Qw6r2o3Uk/4qqR25UG1LiQ8SZvYEKaa5Yw+cCq3waT1YrW0M2p1iXQ+TNOQqhT3Q7fhczh1se0g6qLD+7sBppJfR35LmXJ1FGnU/RoXTSHY5p/lUbvnuTnpLkOu4pGEzp3n3NLfWmu9Iwd64G0npZR7PDuuVpBnw6wOvSCoSomE9WI1mDNrtK/DMRunYKS/yaMIkUoO4Bml50IslPVBY9xJSbtfHSI3QqpKeszQT/JZWL3QB3daKUruTZr3/muRA/7RU7Lznw66iNTepd+TjpHRkx0sqlS2kpTk/KRPPH9v2Lw1Ml/S3kvoVvVpeFCzlZX4/qfftPOB8Sc/0WqdfyUPIk0gvh4erbUGIQcGr99P5+fgaKdb1w5VY1+Ijk2a2XivMqEMP90UlJ7hmzbtJGcweNLO3kJ4X71fhFT4trdjbDUk6pqT+WPCOaS666MEQeKVfMzOb2N5Lkd905iqoC30QO+VBDrk5FjjWzNYHvgbsT1plqiQuM8HzZNPLgMvyMNsOJAf6JDO7StKeBWS90jUBYGYHkUJDrgLeVeMEua+Tsjm0152tgHcCB5YU7/Ci8P6SLwqSDjWzj5EmwO0OfMZSHu7zSC+ixWMhvcgOxSRSyrtfkMJxBpU68l93wiuDBfQg1nWUvJwdyFFncxirfuslQdJUM/ttaYc50ynRw4KkOWVLAn3rNPddeIalTAfPqOCJmdldkt6UP08FjpR0Wd6+s2AP9/7AfqQe3+pEmhNJjvtpJXSztnvslBf5DX4SaYh1Gml4tegbfL9gacWpSaSXwxdLhOhYD5YmHaP+a8DTwB/pHOtaqj4PVe6ZMe2FtKsvCifU+KJQPYfWqMYJpOWVF6z7HEpjKdPRjqQY/R9QcDn6fsHMlpD0rIPumFdr68E5jDrWdZR6Lj3cFf32+PWPVbdLxa+3ncMipLbsw6S5A19R+cQIo8Y7PGMzUoP7LOnN4nvAUqRe170lXVpI9yRSFokngPcCa0p6xVKWg0skvbWEbtbeEfgks0+k+VINwyEvAu9U22TDHDv1pKTfldSvG68YtYp+7Uv+VrRXIr0gTCLFdP+AFCZxXyE914ddnqPQlVIOpZnd123CylDHeqTt8qJQ0X8TqX59kFS/zlO5jDBuZDs/xKyezpatDXhN0ptdTmwA6VKPZ2awkPTums9njmJdR6nxPlI92oI0avUD4NuqYZn2rH/0EIcl6QsFtZcgOel7kl5OThoPcwW8wzNOIaVzWYyUN3gHSTdYWoHpPPKKQIV0P0gaIqimX1uDwknUJf0M+FlJjS7cSOdUQi+QQhZqX1GtMGPOxzhGPJb8xcyuJ+Wi/hGwv1Je2dJ4pWsCyjnFI+BpM9tE0k3VnWa2McmZLUktD9UqedSi9SI6g/SAf6cKZx1yppOda7u3m4R6sFpbj8/nWdIkuWIjwJIuBi6u9HAfCixrZt+kYA93Rb/rpE4zO6SUrpl9CdgFOB14kzos5d2vePc0VzMMzNYzUzLbg6Ucukeqba333HtynKRiDqSllCpHUMnTDJwoqdSS4S3dmyVt3OXYzHCVQaEP3uAnMGvJ3/WpYcnfrLs18MuS4U3D6NeWrqmiOZ3OE4qLpSLLupuQhhPPZlaM61uBvUkTI2/s8qfjkjyZ+DzSqMVdlf21zPT3xuPebhLeo4P9Qh093CM8j0clrVTot18D/k7KSFNL+she4d3T/Frlc3ugf8mH/rLtDjOApLvyjP8iWFpN6QBSeEarB/CtwAlmtoKk00tpM/TS4QsU1HWhD97gXZb8BbYBtrbOS6MXmZXc5WFX22QWSYsM/60iujdlx/lwUkweJOd509IxeR4vCtU4y04z/Xut1w9439sNw3t0sC+oo4d7hBQbHZVUOvFBMbx7mmeQQiSM5Li1llA0YH5JRZaizTNE1+hy7EFJb+x0rAe695LCQZ5t278kaQGOkjGQ5wFXq21FJzP7MGmItfiKXt7U/QZvr1/y96ekCZ9/KKj58Q67Z85KlrRwAU3XySxeWFoq+0uknuVppHZrWdKiLieY2YZqy8k+nmliT2BT720PvEcHg9kp2dM8num77Bl14OVAOk8cWpa0lPTLzD6UPC8pZ/WTpbSbiM2+5O8PVMOSvx3OoZZZyU192FlaYnlB0spZ0/O+RYEvk2J+3zVINmiiA9nUe9uTujNYNJlhRqwWUKHVVMczTXWaXRxIS4uq7K+21cIsrSp2hqRNSui2ab2d5MxBWjr86tKaTcQclvytaLvMSm7aw87MHgTWaI8fz/HsfyJPbHY5uQI02YFs2r3dL/RLfG8QtGik09yibgfSzLYCvk/K0HEryZnaGJgM7CXpupL6weDTNiv5VK9ZyU142JnZA5LWnNNj452mO5BNuLeDIOhMo51mD3Iv90HMnqf5VElP+Z1VMCiM51nJ4w0z+zFpqdvvtu3fC9hN0k4+Z1Yf4UAGQdAkwmmuETPbCVhB0ql5+yZgaZJz80lJP/I8vyAIRo6ZLU/KGvFXZh85WoAU5lVswmcQBEFQP+E014iZ/YqUv/X3eft20vDmwsB3JG3neX5BEMw5ZrYtaeTISGFeVzmfUhAEQVCAmBlZL/O2HObMdTn93LM5TjAIgnFGngsRE2qDIAgGnHGbYHqcMtsCI5L+o7K5dM2aWcb0AAAAnElEQVTnEgRBEARBEIyQcJrr5ca8KuBsmNkBwE0O5xMEQRAEQRCMgIhprhEzWwb4MSm7wdS8eyNgPmDnyKARBEEQBEHQn4TT7EBl4hDEAiNBEARBEAR9TzjNQRAEQRAEQTAMEdMcBEEQBEEQBMMQTnMQBEEQBEEQDEM4zUEQBEEQBEEwDOE0B0EQBEEQBMEwhNMcBEEQBEEQBMPw//fzy9OPdwxbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2b9b7f3c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import seaborn\n",
    "import matplotlib.pyplot as plt\n",
    "seaborn.mpl.rcParams['figure.figsize'] = (12.0, 8.0)\n",
    "seaborn.mpl.rcParams['savefig.dpi'] = 90\n",
    "\n",
    "\"\"\"\n",
    "this cell generates graphs for coefficients for first and second eigenvectors\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "# define parameters for wrappers\n",
    "\n",
    "top_n = 25 # top 30 market cap tech tickers to choose from\n",
    "filename = './companylist.csv'\n",
    "pools = get_tech_tickers(filename, top_n)\n",
    "pools.remove('GOOG')\n",
    "# dates = ['20150102','20170103','20170104']\n",
    "dates = ['20150102']\n",
    "start_time = '9am'\n",
    "end_time = '4pm'\n",
    "mtype = 'T'\n",
    "group = 'directs'\n",
    "# align_by_sec_interval_list = [1,5,10,20,30,60,120]\n",
    "align_by_sec_interval_list = [30]\n",
    "output_pairs = False\n",
    "thre = 1\n",
    "# which eigen vector to plot\n",
    "num_eigenvec = 1 # 1= second eigen vector\n",
    "\n",
    "# to use\n",
    "if __name__ == '__main__':\n",
    "    res = {}\n",
    "    for date in dates:\n",
    "        res[date] = {}\n",
    "        for align_by_sec_interval in align_by_sec_interval_list:\n",
    "            print '\\n\\n'\n",
    "            print 'pulling data on date: ', date, 'with align time interval of: ', align_by_sec_interval\n",
    "            df = get_pool_data(pools, date, group, start_time, end_time, mtype, align_by_sec_interval)\n",
    "            eig_pair, x_axis_names = pca(df, output_pairs = output_pairs, thre = thre)\n",
    "            res[date][align_by_sec_interval] = eig_pair\n",
    "            (eig_val, eig_vec) = zip(*eig_pair)\n",
    "\n",
    "            evec_list = zip(eig_vec[num_eigenvec], x_axis_names)\n",
    "            evec_list.sort(key=lambda tup:tup[0], reverse = True)\n",
    "            eig_vec_new, x_axis_names = zip(*evec_list)\n",
    "           \n",
    "            df = pd.DataFrame(columns = x_axis_names)\n",
    "            df.loc[0] = eig_vec_new\n",
    "                \n",
    "            my_xticks = df.columns.tolist()\n",
    "            x = np.array(range(len(my_xticks)))\n",
    "            y = np.array(df.loc[0].values)\n",
    "            plt.xticks(x, my_xticks, rotation='vertical')\n",
    "            plt.grid(True)\n",
    "            plt.title('Second Eigenvector from Top 25 Market Cap Tech Stocks for 30 Second Interval')\n",
    "            plt.plot(x, y)\n",
    "            plt.show()\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "pulling data on date:  20150102 with align time interval of:  10\n",
      "Aligning by time interval: 10 seconds\n",
      "getting data from: AAPL\n",
      "getting data from: MSFT\n",
      "getting data from: GOOGL\n",
      "getting data from: FB\n",
      "getting data from: INTC\n",
      "getting data from: CSCO\n",
      "getting data from: NVDA\n",
      "getting data from: ADBE\n",
      "getting data from: TXN\n",
      "getting data from: AVGO\n",
      "getting data from: QCOM\n",
      "getting data from: MU\n",
      "getting data from: BIDU\n",
      "getting data from: AABA\n",
      "Ticker not found in the database!\n",
      "getting data from: ADP\n",
      "getting data from: ATVI\n",
      "getting data from: AMAT\n",
      "getting data from: INTU\n",
      "getting data from: CTSH\n",
      "getting data from: EA\n",
      "getting data from: NXPI\n",
      "getting data from: ADI\n",
      "getting data from: LRCX\n",
      "getting data from: ADSK\n",
      "\n",
      "merge complete.\n",
      "\n",
      "length of extracted eigenvectors is: 23\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2b9a626350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import seaborn\n",
    "import matplotlib.pyplot as plt\n",
    "seaborn.mpl.rcParams['figure.figsize'] = (12.0, 8.0)\n",
    "seaborn.mpl.rcParams['savefig.dpi'] = 90\n",
    "\n",
    "\"\"\"\n",
    "this cell generates graphs for eigenvalues for correlation matrix\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "# define parameters for wrappers\n",
    "\n",
    "top_n = 25 # top 30 market cap tech tickers to choose from\n",
    "filename = './companylist.csv'\n",
    "pools = get_tech_tickers(filename, top_n)\n",
    "pools.remove('GOOG')\n",
    "# dates = ['20150102','20170103','20170104']\n",
    "dates = ['20150102']\n",
    "start_time = '9am'\n",
    "end_time = '4pm'\n",
    "mtype = 'T'\n",
    "group = 'directs'\n",
    "# align_by_sec_interval_list = [1,5,10,20,30,60,120]\n",
    "align_by_sec_interval_list = [10]\n",
    "output_pairs = False\n",
    "thre = 1\n",
    "# which eigen vector to plot\n",
    "num_eigenvec = 1 # 1= second eigen vector\n",
    "\n",
    "# to use\n",
    "if __name__ == '__main__':\n",
    "    res = {}\n",
    "    for date in dates:\n",
    "        res[date] = {}\n",
    "        for align_by_sec_interval in align_by_sec_interval_list:\n",
    "            print '\\n\\n'\n",
    "            print 'pulling data on date: ', date, 'with align time interval of: ', align_by_sec_interval\n",
    "            df = get_pool_data(pools, date, group, start_time, end_time, mtype, align_by_sec_interval)\n",
    "            eig_pair, x_axis_names = pca(df, output_pairs = output_pairs, thre = thre)\n",
    "            res[date][align_by_sec_interval] = eig_pair\n",
    "            (eig_val, eig_vec) = zip(*eig_pair)\n",
    "    \n",
    "            eig_val = eig_val/np.sum(eig_val) # convert to percentage\n",
    "            x = np.array(range(len(eig_val)))\n",
    "            y = np.array(eig_val)\n",
    "            plt.xticks(x)\n",
    "            plt.grid(True)\n",
    "            plt.title('Eigenvalues of Correlation Matrix of Top 25 Market Cap Tech Stocks for 10 Seconds Interval')\n",
    "            plt.bar(x, y)\n",
    "            plt.show()\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2b99acde90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.array(range(len(eig_val)))\n",
    "y = np.array(eig_val)\n",
    "plt.xticks(x)\n",
    "plt.grid(True)\n",
    "plt.title('Eigenvalues of Correlation Matrix of Top 25 Market Cap Tech Stocks for 30 Seconds Interval')\n",
    "plt.bar(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# meeting notes - graph on correlated price of a pairs\n",
    "# a table on most correlated pairs by different time interval \n",
    "# "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python2",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
